Chemical named entities recognition: a review on approaches and applications
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paper_title: ChemicalTagger: A tool for semantic text-mining in chemistry

paper_content:

The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.
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paper_title: Detection of IUPAC and IUPAC-like chemical names

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Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. ::: ::: Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. ::: ::: Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component. ::: ::: Contact: roman.klinger@scai.fraunhofer.de
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paper_title: ChemicalTagger: A tool for semantic text-mining in chemistry

paper_content:

The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.
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paper_title: Mining chemical structural information from the drug literature.

paper_content:

It is easier to find too many documents on a life science topic than to find the right information inside these documents. With the application of text data mining to biological documents, it is no surprise that researchers are starting to look at applications that mine out chemical information. The mining of chemical entities--names and structures--brings with it some unique challenges, which commercial and academic efforts are beginning to address. Ultimately, life science text data mining applications need to focus on the marriage of biological and chemical information.
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paper_title: An evaluation of online databases by methods of lattice theory

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Abstract The evaluation of databases is of great importance for potential users. The usual technique to define a ranking index however is not helpfull because users may have different interests concerning databases. Therefore evaluation techniques are needed, which do not mask different elements of information performing a ranking. The appropriate technique is given by the theory of order relation which leads to partially ordered sets. Partially ordered sets can be visualized by Hasse diagrams. In this contribution 15 databases are characterised by five quality criteria and the Hasse diagram shows that there are four databases which are best suitable with respect to different aspects, namely CAS, ECDIN, BEIISTEIN and HSDB. These four databases are further investigated by the formal concept analysis. A comprehensive diagram shows the specialities of each of these four databases and logical rules are automatically derived, which show that there are interdependencies between qualities within the context of the four priority databases. For example a good quality with respect to the use of chemicals implies a good quality of the general extent of the database with respect to the chemicals.
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paper_title: Mining chemical structural information from the drug literature.

paper_content:

It is easier to find too many documents on a life science topic than to find the right information inside these documents. With the application of text data mining to biological documents, it is no surprise that researchers are starting to look at applications that mine out chemical information. The mining of chemical entities--names and structures--brings with it some unique challenges, which commercial and academic efforts are beginning to address. Ultimately, life science text data mining applications need to focus on the marriage of biological and chemical information.
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paper_title: RelEx---Relation extraction using dependency parse trees

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Motivation: The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases. ::: ::: Results: We developed RelEx, an approach for relation extraction from free text. It is based on natural language preprocessing producing dependency parse trees and applying a small number of simple rules to these trees. We applied RelEx on a comprehensive set of one million MEDLINE abstracts dealing with gene and protein relations and extracted ~150 000 relations with an estimated perfomance of both 80% precision and 80% recall. ::: ::: Availability: The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our website (http://www.bio.ifi.lmu.de/publications/RelEx/). ::: ::: Contact: katrin.fundel@bio.ifi.lmu.de
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paper_title: Automatic Extraction of semantic relations between medical entities: Application to the treatment relation

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Information extraction is a complex task which is necessary to develop highprecision information retrieval tools. In this paper, we present MeTAE, a platform to extract medical entities and the medical relations linking them. The proposed approach relies on linguistic patterns and domain knowledge and consists in two steps: (i) recognition of medical entities and (ii) identification of the correct semantic relation between each pair of entities. The first step is achieved by an enhanced use of MetaMap which improves the precision obtained by MetaMap by 19.59% in our evaluation. The second step relies on linguistic patterns which are built semiautomatically from a corpus selected according to semantic criteria. We evaluate our system’s ability to identify medical entities of 16 types. We also evaluate the extraction of treatment relations between a treatment (e.g., medication) and a problem (e.g., disease): we obtain 75.72% of precision and 60.46% of recall. We achieve encouraging results w.r.t similar research works in the literature.
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paper_title: A Verb-Centric Approach for Relationship Extraction in Biomedical Text

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Advances in biomedical technology and research have resulted in a large number of research findings, which are primarily published in unstructured text such as journal articles. Text mining techniques have been thus employed to extract knowledge from such data. In this article we focus on the task of identifying and extracting relations between bio-entities such as green tea and breast cancer. Unlike previous work that employs heuristics such as co-occurrence patterns and handcrafted syntactic rules, we propose a verb-centric algorithm. This algorithm identifies and extracts the main verb(s) in a sentence, therefore, it does not require the usage of predefined rules or patterns. Using the main verb(s) it then extracts the two involved entities of a relationship. The biomedical entities are identified using a dependence parse tree by applying syntactic and linguistic features such as preposition phrases and semantic role analysis. The proposed verb-centric approach can effectively handle complex sentence structures such as clauses and conjunctive sentences. We evaluate the algorithm on several data sets and achieve an average F-score of 0.905, which is significantly higher than that of previous work.
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paper_title: Name=Struct: A Practical Approach to the Sorry State of Real-Life Chemical Nomenclature

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The interpretation (automatic or otherwise) of chemical names is complicated by the poor quality of such names in common usage. A practical interpretation system for chemical names must consider not only the published rules for chemical nomenclature but also their misinterpretations, corruptions, and popular extensions. This paper describes the design of Name=Struct, an automated system for the conversion of chemical names to their corresponding structures, and discusses the nomenclature interpretation problems addressed in its development. The comprehensiveness and accuracy of Name=Struct are analyzed in several contexts.
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paper_title: Reconstruction of Chemical Molecules from Images

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We have developed a system for the automatic reconstruction of chemical molecules from images. The system takes as input an electronically produced image of a chemical molecule and produces an SDF file containing the complete chemical description of the molecule. The SDF file can then be read and used by most chemical computer programs. Our system finds extensive application in information extraction problems where the molecule images contained in chemical documents need to be rendered computer readable. We have benchmarked our system against a commercially available product and have also tested it using chemical databases of several thousand images. The system can be parameterized to reconstruct images of different sources and different characteristics.
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paper_title: Overview of Biomedical Relations Extraction using Hybrid Rule-based Approaches

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Unstructured text documents are the major source of knowledge in biomedical fields. These huge amounts of information cause very difficult task of extraction or classification.Therefore, there is a need for knowledge discovery and text mining tools in this field. A lot ::: of works have been done on relation extraction in ::: biomedical field. However, each of them was implemented in ::: three major types of techniques separately i.e.co-occurrence,kernel based and rule based methods. There are many variants of these algorithms have been developed but the combination of it has not been verified yet. In this paper we will compare each of those three methods and propose a new combination of relation extraction method between medical and biological entities from biomedical documents.Furthermore, a lot of researches have been done on ::: biomedical binary relation such as protein-protein and ::: gene-protein relations and few researches were on complex ::: relations such as metabolic pathways. However, in this work ::: we will discuss the overview a combination of three methods ::: called as hybrid rule-based to extract complex and simple ::: relations.
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paper_title: Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge

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Divide and conquer—the strategy that science uses to cope with the mountains of printed matter it produces—appears on the surface to serve us well. Science organizes itself into manageable units—scientific specialties—and so its literature is created and assimilated in manageable chunks or units. But a few clouds on the horizon ought not to go unexamined. First, most of the units are no doubt logically related to other units. Second, there are far more combinations of units, therefore far more potential relationships among the units, than there are units. Third, the system is not organized to cope with combinations. I suggest that important relationships might be escaping our notice. Individual units of literature are created to some degree independently of one another, and, insofar as that is so, the logical connections among the units, though inevitable, may be unintended by and even unknown to their creators. Until those fragments, like scattered pieces of a puzzle, are brought together, the relationships among them may remain undiscovered—even though the isolated pieces might long have been public knowledge. My purpose in this essay is to show, by means of an example, how this might happen. I shall identify two units of literature that are logically connected but noninteractive; neither seems to acknowledge the other to any substantial degree. Yet the logical connections, once apparent, lead to a potentially useful and possibly new hypothesis.
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paper_title: Automated extraction of chemical structure information from digital raster images

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BackgroundTo search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated.ResultsThis paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns.ConclusionThe availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research articles.
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paper_title: Named Entity Recognition Approaches

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Recognizing and extracting exact name entities, like Persons, Locations and Organizations are very useful to mining information from text. Learning to extract names in natural language text is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics. Nowadays more researchers use different methods such as Rule-base NER, Machine Learning-base NER and Hybrid NER, to identify names from text. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference (MUC) named entity definition and its standard data set to find their strength and weakness of each ::: these methods. We proposed a robust and novel Machine ::: Learning Based method called Fuzzy support Vector Machine ::: (FSVM) for NER.
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paper_title: A dictionary to identify small molecules and drugs in free text

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Motivation: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. ::: ::: Results: We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary. ::: ::: Availability: The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist. ::: ::: Contact: k.hettne@erasmusmc.nl ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.
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paper_title: EBIMed—text crunching to gather facts for proteins from Medline

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Summary: To allow efficient and systematic retrieval of statements from Medline we have developed EBIMed, a service that combines document retrieval with co-occurrence-based analysis of Medline abstracts. Upon keyword query, EBIMed retrieves the abstracts from EMBL-EBI's installation of Medline and filters for sentences that contain biomedical terminology maintained in public bioinformatics resources. The extracted sentences and terminology are used to generate an overview table on proteins, Gene Ontology (GO) annotations, drugs and species used in the same biological context. All terms in retrieved abstracts and extracted sentences are linked to their entries in biomedical databases. We assessed the quality of the identification of terms and relations in the retrieved sentences. More than 90% of the protein names found indeed represented a protein. According to the analysis of four protein--protein pairs from the Wnt pathway we estimated that 37% of the statements containing such a pair mentioned a meaningful interaction and clarified the interaction of Dkk with LRP. We conclude that EBIMed improves access to information where proteins and drugs are involved in the same biological process, e.g. statements with GO annotations of proteins, protein--protein interactions and effects of drugs on proteins. ::: ::: Availability: Available at http://www.ebi.ac.uk/Rebholz-srv/ebimed ::: ::: Supplementary Data: Supplementary Data are available at Bioinformatics online. ::: ::: Contact: [email protected]
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paper_title: Association rules mining for name entity recognition

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We propose a new name entity class extraction method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.
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paper_title: An Algorithm that Learns What's in a Name

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In this paper, we present IdentiFinderTM, a hidden Markov model that learns to recognize and classify names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the Sixth and Seventh Message Understanding Conferences [MUC-6, MUC-7] and broadcast news) and in Spanish (based on data distributed through the First Multilingual Entity Task [MET-1]), and on speech input (based on broadcast news). We report results here on standard materials only to quantify performance on data available to the community, namely, MUC-6 and MET-1. Results have been consistently better than reported by any other learning algorithm. IdentiFinder‘s performance is competitive with approaches based on handcrafted rules on mixed case text and superior on text where case information is not available. We also present a controlled experiment showing the effect of training set size on performance, demonstrating that as little as 100,000 words of training data is adequate to get performance around 90% on newswire. Although we present our understanding of why this algorithm performs so well on this class of problems, we believe that significant improvement in performance may still be possible.
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paper_title: A maximum entropy approach to named entity recognition

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This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. ::: Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. ::: MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. ::: Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese.
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paper_title: Named Entity Recognition: A Maximum Entropy Approach Using Global Information

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This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentence-based classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on MUC-6 and MUC-7 test data.
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paper_title: Named Entity Recognition: Exploring Features

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We study a comprehensive set of features used in supervised named entity recognition. We explore various combinations of features and compare their impact on recognition performance. We build a conditional random field based system that achieves 91.02% F1-measure on the CoNLL 2003 (Sang and Meulder, 2003) dataset and 81.4% F1-measure on the OntoNotes version 4 (Hovy et al., 2006) CNN dataset, which, to our knowledge, displays the best results in the state of the art for those benchmarks respectively. We demonstrate statistical significance of the boost of performance over the previous top performing system. We also obtained 74.27% F1-measure on NLPBA 2004 (Kim et al., 2004) dataset.
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paper_title: Extraction and search of chemical formulae in text documents on the web

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Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) designranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines(SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.
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paper_title: Biomedical Named Entity Recognition Based on Classifiers Ensemble

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In this paper, we present classifiers ensemble approaches for biomedical named entity recognition. Generalized Winnow, Conditional Random Fields, Support Vector Machine, and Maximum Entropy are combined through three different strategies. We demonstrate the effectiveness of classifiers ensemble strategies and compare its performances with standalone classifier systems. In the experiments on the JNLPBA 2004 evaluation data, our best system achieves an F-score of 77.57%, which is better than most state of the art systems. The experiment show that our proposed classifiers ensemble method especially the stacking method can lead to significant improvement in performances of biomedical named entity recognition.
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paper_title: I.: FreeLing 2.1: Five Years of Open-Source Language Processing Tools

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FreeLing is an open-source multilingual language processing library providing a wide range of language analyzers for ::: several languages. It offers text processing and language annotation facilities to natural language processing application ::: developers, simplifying the task of building those applications. FreeLing is customizable and extensible. Developers can ::: use the default linguistic resources (dictionaries, lexicons, grammars, etc.) directly, or extend them, adapt them to specific ::: domains, or even develop new ones for specific languages. ::: This paper overviews the recent history of this tool, summarizes the improvements and extensions incorporated in the latest ::: version, and depicts the architecture of the library. Special focus is brought to the fact and consequences of the library ::: being open-source: After five years and over 35,000 downloads, a growing user community has extended the initial three ::: languages (English, Spanish and Catalan) to eight (adding Galician, Italian, Welsh, Portuguese, and Asturian), proving that ::: the collaborative open model is a productive approach for the development of NLP tools and resources.
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paper_title: CheNER: chemical named entity recognizer

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Motivation: Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemical compounds, which due to the complex morphology of IUPAC names requires more advanced techniques than that of brand names. Results: We present CheNER, a tool for automated identification of systematic IUPAC chemical mentions. We evaluated different systems using an established literature corpus to show that CheNER has a superior performance in identifying IUPAC names specifically, and that it makes better use of computational resources. Availability and implementation: http://metres.udl.cat/index.php/9download/4-chener, http://chener.bioinfo.cnio.es/ Contact: miguel.vazquez@cnio.es Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: WCCL: a morpho-syntactic feature toolkit

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The paper presentsWCCL, a new formalism and toolkit for constructing morpho-syntactic features, a crucial task for many natural language processing algorithms. One existing solution, JOSKIPI, is analysed from two perspectives: features of the formalism as well as software engineering-related issues. Then we propose its successor. A short case study follows, exemplifying the improvement enabled by using rich features expressed with WCCL. The formalism is targeted at Polish, although it seems well suited for any inflectional language.
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paper_title: Types of Machine Learning Algorithms

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• Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.
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paper_title: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

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We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
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paper_title: Identification of chemical entities in patent documents

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Biomedical literature is an important source of information for chemical compounds. However, different representations and nomenclatures for chemical entities exist, which makes the reference of chemical entities ambiguous. Many systems already exist for gene and protein entity recognition, however very few exist for chemical entities. The main reason for this is the lack of corpus to train named entity recognition systems and perform evaluation. ::: ::: In this paper we present a chemical entity recognizer that uses a machine learning approach based on conditional random fields (CRF) and compare the performance with dictionary-based approaches using several terminological resources. For the training and evaluation, a gold standard of manually curated patent documents was used. While the dictionary-based systems perform well in partial identification of chemical entities, the machine learning approach performs better (10% increase in F-score in comparison to the best dictionary-based system) when identifying complete entities.
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paper_title: Chemical Entity Recognition and Resolution to ChEBI

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Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks.
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paper_title: Mining, indexing, and searching for textual chemical molecule information on the web

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Current search engines do not support user searches for chemical entities (chemical names and formulae) beyond simple keyword searches. Usually a chemical molecule can be represented in multiple textual ways. A simple keyword search would retrieve only the exact match and not the others. We show how to build a search engine that enables searches for chemical entities and demonstrate empirically that it improves the relevance of returned documents. Our search engine first extracts chemical entities from text, performs novel indexing suitable for chemical names and formulae, and supports different query models that a scientist may require. We propose a model of hierarchical conditional random fields for chemical formula tagging that considers long-term dependencies at the sentence level. To substring searches of chemical names, a search engine must index substrings of chemical names. Indexing all possible sub-sequences is not feasible in practice. We propose an algorithm for independent frequent subsequence mining to discover sub-terms of chemical names with their probabilities. We then propose an unsupervised hierarchical text segmentation (HTS) method to represent a sequence with a tree structure based on discovered independent frequent subsequences, so that sub-terms on the HTS tree should be indexed. Query models with corresponding ranking functions are introduced for chemical name searches. Experiments show that our approaches to chemical entity tagging perform well. Furthermore, we show that index pruning can reduce the index size and query time without changing the returned ranked results significantly. Finally, experiments show that our approaches out-perform traditional methods for document search with ambiguous chemical terms.
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paper_title: Maximum Entropy Markov Models for Information Extraction and Segmentation

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Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We present positive experimental results on the segmentation of FAQ’s.
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paper_title: Extracting named entities using support vector machines

paper_content:

Identifying proper names, like gene names, DNAs, or proteins is useful to help researchers to mining the text information. Learning to extract proper names in natural language text is a named entity recognition (NER) task. Previous studies focus on combining abundant human made rules, trigger words, to enhance the system performance. However these methods require domain experts to build up these rules and word set which relies on lots of human efforts. In this paper, we present a robust named entity recognition system based on support vector machines (SVM). By integrating with rich feature set and the proposed mask method, the system performance is satisfactory on the MUC-7 and biology named entity recognition tasks which outperforms famous machine learning-based method, such as hidden markov model (HMM), and maximum entropy model (MEM). We compare our method to previous systems that were performed on the same data set. The experiments show that when training with the MUC-7 data set, our system achieves 86.4 in F(β=1) rate and 81.57 for the biology corpus. Besides, our named entity system is able to handle real time processing applications, the turn around time on a 63 K words document set is less than 30 seconds.
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paper_title: Conditional Random Fields vs. Hidden Markov Models in a biomedical Named Entity Recognition task

paper_content:

With a recent quick development of a molecular biology domain the Information Extraction (IE) methods become very useful. Named Entity Recognition (NER), that is considered to be the easiest task of IE, still remains very challenging in molecular biology domain because of the complex structure of biomedical entities and the lack of naming convention. In this paper we apply two popular sequence labeling approaches: Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) to solve this task. We exploit different stategies to construct our biomedical Named Entity (NE) recognizers which take into account special properties of each approach. Although the CRF-based model has obtained much better results in the F-score, the advantage of the CRF approach remains disputable, since the HMM-based model has achieved a greater recall for some biomedical classes. This fact makes us think about a possibility of an effective combination of these models.
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paper_title: Detection of IUPAC and IUPAC-like chemical names

paper_content:

Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. ::: ::: Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. ::: ::: Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component. ::: ::: Contact: roman.klinger@scai.fraunhofer.de
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paper_title: Fextor: A Feature Extraction Framework for Natural Language Processing: A Case Study in Word Sense Disambiguation, Relation Recognition and Anaphora Resolution

paper_content:

Feature extraction from text corpora is an important step in Natural Language Processing (NLP), especially for Machine Learning (ML) techniques. Various NLP tasks have many common steps, e.g. low level act of reading a corpus and obtaining text windows from it. Some high-level processing steps might also be shared, e.g. testing for morpho-syntactic constraints between words. An integrated feature extraction framework removes wasteful redundancy and helps in rapid prototyping.
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paper_title: Types of Machine Learning Algorithms

paper_content:

• Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.
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paper_title: Named Entity Recognition Approaches

paper_content:

Recognizing and extracting exact name entities, like Persons, Locations and Organizations are very useful to mining information from text. Learning to extract names in natural language text is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics. Nowadays more researchers use different methods such as Rule-base NER, Machine Learning-base NER and Hybrid NER, to identify names from text. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference (MUC) named entity definition and its standard data set to find their strength and weakness of each ::: these methods. We proposed a robust and novel Machine ::: Learning Based method called Fuzzy support Vector Machine ::: (FSVM) for NER.
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paper_title: Semi-Supervised Named Entity Recognition: Learning to Recognize 100 Entity Types with Little Supervision

paper_content:

Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. There has been growing interest in this field of research since the early 1990s. In this thesis, we document a trend moving away from handcrafted rules, and towards machine learning approaches. Still, recent machine learning approaches have a problem with annotated data availability, which is a serious shortcoming in building and maintaining large-scale NER systems. ::: In this thesis, we present an NER system built with very little supervision. Human supervision is indeed limited to listing a few examples of each named entity (NE) type. First, we introduce a proof-of-concept semi-supervised system that can recognize four NE types. Then, we expand its capacities by improving key technologies, and we apply the system to an entire hierarchy comprised of 100 NE types. ::: Our work makes the following contributions: the creation of a proof-of-concept semi-supervised NER system; the demonstration of an innovative noise filtering technique for generating NE lists; the validation of a strategy for learning disambiguation rules using automatically identified, unambiguous NEs; and finally, the development of an acronym detection algorithm, thus solving a rare but very difficult problem in alias resolution. ::: We believe semi-supervised learning techniques are about to break new ground in the machine learning community. In this thesis, we show that limited supervision can build complete NER systems. On standard evaluation corpora, we report performances that compare to baseline supervised systems in the task of annotating NEs in texts.
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paper_title: Chemistry-specific Features and Heuristics for Developing a CRF-based Chemical Named Entity Recogniser

paper_content:

We describe and compare methods developed for the BioCreative IV chemical compound and drug name recognition (CHEMDNER) task. The presented conditional random fields (CRF)-based named entity recogniser employs a statistical model trained on domain-specific features, in addition to those typically used in biomedical NERs. In order to increase recall, two heuristics-based post-processing steps were introduced, namely, abbreviation recognition and re-labelling based on a token’s chemical segment composition. The chemical NER was used to generate predictions for both the Chemical Entity Mention recognition (CEM) and Chemical Document Indexing (CDI) subtasks of the challenge. Results obtained from training a model on the provided training set and testing on the development set show that employing chemistryspecific features and heuristics leads to an increase in performance in both subtasks.
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paper_title: Drug name recognition and classification in biomedical texts. A case study outlining approaches underpinning automated systems.

paper_content:

This article presents a system for drug name recognition and classification in biomedical texts. The system combines information obtained by the Unified Medical Language System (UMLS) MetaMap Transfer (MMTx) program and nomenclature rules recommended by the World Health Organization (WHO) International Nonproprietary Names (INNs) Program to identify and classify pharmaceutical substances. Moreover, the system is able to detect possible candidates for drug names that have not been detected by MMTx program by applying these rules, achieving, in this way, a broader coverage. This work is the first step in a method for automatic detection of drug interactions from biomedical texts, a specific type of adverse drug event of special interest in patient safety.
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paper_title: ChemSpot: a hybrid system for chemical named entity recognition

paper_content:

Motivation: The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools. Results: We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and International Union of Pure and Applied Chemistry entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F1 measure of 68.1% on the SCAI corpus, outperforming the only other freely available chemical NER tool, OSCAR4, by 10.8 percentage points. Availability: ChemSpot is freely available at: http://www.informatik.hu-berlin.de/wbi/resources Contact: leser@informatik.hu-berlin.de
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paper_title: Highthroughput identification of chemistry in life science texts

paper_content:

OSCAR3 is an open extensible system for the automated annotation of chemistry in scientific articles, which can process thousands of articles per hour. This XML annotation supports applications such as interactive browsing and chemically-aware searching, and has been designed for integration with larger text-analysis systems. We report its application to the high-throughput analysis of the small-molecule chemistry content of texts in life sciences, such as PubMed abstracts.
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paper_title: Mining connections between chemicals, proteins, and diseases extracted from Medline annotations

paper_content:

The biomedical literature is an important source of information about the biological activity and effects of chemicals. We present an application that extracts terms indicating biological activity of chemicals from Medline records, associates them with chemical name and stores the terms in a repository called ChemoText. We describe the construction of ChemoText and then demonstrate its utility in drug research by employing Swanson's ABC discovery paradigm. We reproduce Swanson's discovery of a connection between magnesium and migraine in a novel approach that uses only proteins as the intermediate B terms. We validate our methods by using a cutoff date and evaluate them by calculating precision and recall. In addition to magnesium, we have identified valproic acid and nitric oxide as chemicals which developed links to migraine. We hypothesize, based on protein annotations, that zinc and retinoic acid may play a role in migraine. The ChemoText repository has promise as a data source for drug discovery.
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paper_title: Biomedical Named Entity Recognition: A Survey of Machine-Learning Tools

paper_content:

It is well known that the rapid growth and dissemination of the Internet has resulted in huge amounts of information generated and shared, available in the form of textual data, images, videos or sounds. This overwhelming surge of data is also true for specific areas such as biomedicine, where the number of published documents, such as articles, books and technical reports, is increasing exponentially. For instance, the MEDLINE literature database contains over 20 million references to journal papers, covering a wide range of biomedical fields. In order to organize and manage these data, several manual curation efforts have been set up to identify, in texts, information regarding entities (e.g. genes and proteins) and their relations (e.g. protein-protein interactions). The extracted information is stored in structured knowledge resources, such as Swiss-Prot [1] and GenBank [2]. However, the ef‐ fort required to continually update these databases makes this a very demanding and ex‐ pensive task, naturally leading to increasing interest in the application of Text Mining (TM) systems to help perform those tasks.
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paper_title: 490 Text analytics for life science using the Unstructured Information Management Architecture

paper_content:

Biomedical text plays a fundamental role in knowledge discovery in life science, in both basic research (in the field of bioinformatics) and in industry sectors devoted to improving medical practice, drug development, and health care (such as medical informatics, clinical genomics, and other sectors). Several groups in the IBM Research Division are collaborating on the development of a prototype system for text analysis, search, and text-mining methods to support problem solving in life science. The system is called "BioTeKS" ("Biological Text Knowledge Services"), and it integrates research technologies from multiple IBM Research labs. BioTeKS is also the first major application of the UIMA (Unstructured Information Management Architecture) initiative also emerging from IBM Research. BioTeKS is intended to analyze biomedical text such as MEDLINETM abstracts, medical records, and patents; text is analyzed by automatically identifying terms or names corresponding to key biomedical entities (e.g., "genes," "proteins," "compounds," or "drugs") and concepts or facts related to them. In this paper, we describe the value of text analysis in biomedical research, the development of the BioTeKS system, and applications which demonstrate its functions.
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paper_title: Extracting and characterizing gene-drug relationships from the literature.

paper_content:

A fundamental task of pharmacogenetics is to collect and classify relationships between genes and drugs. Currently, this useful information has not been comprehensively aggregated in any database and remains scattered throughout the published literature. Although there are efforts to collect this in
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paper_title: Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications

paper_content:

Providing prior knowledge about biological properties of chemicals, such as kinetic values, protein targets, or toxic effects, can facilitate many aspects of drug development. Chemical information is rapidly accumulating in all sorts of free text documents like patents, industry reports, or scientific articles, which has motivated the development of specifically tailored text mining applications. Despite the potential gains, chemical text mining still faces significant challenges. One of the most salient is the recognition of chemical entities mentioned in text. To help practitioners contribute to this area, a good portion of this review is devoted to this issue, and presents the basic concepts and principles underlying the main strategies. The technical details are introduced and accompanied by relevant bibliographic references. Other tasks discussed are retrieving relevant articles, identifying relationships between chemicals and other entities, or determining the chemical structures of chemicals mentioned in text. This review also introduces a number of published applications that can be used to build pipelines in topics like drug side effects, toxicity, and protein-disease-compound network analysis. We conclude the review with an outlook on how we expect the field to evolve, discussing its possibilities and its current limitations.
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paper_title: Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

paper_content:

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.
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paper_title: Relating drug–protein interaction network with drug side effects

paper_content:

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. ::: ::: Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles. ::: ::: Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/. ::: ::: Availability: Software is available at the above supplementary website. ::: ::: Contact:yamanishi@bioreg.kyushu-u.ac.jp, or goto@kuicr.kyoto-u.ac.jp
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paper_title: Drug name recognition and classification in biomedical texts. A case study outlining approaches underpinning automated systems.

paper_content:

This article presents a system for drug name recognition and classification in biomedical texts. The system combines information obtained by the Unified Medical Language System (UMLS) MetaMap Transfer (MMTx) program and nomenclature rules recommended by the World Health Organization (WHO) International Nonproprietary Names (INNs) Program to identify and classify pharmaceutical substances. Moreover, the system is able to detect possible candidates for drug names that have not been detected by MMTx program by applying these rules, achieving, in this way, a broader coverage. This work is the first step in a method for automatic detection of drug interactions from biomedical texts, a specific type of adverse drug event of special interest in patient safety.
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paper_title: A Semi-Supervised Pattern-Learning Approach to Extract Pharmacogenomics-Specific Drug-Gene Pairs from Biomedical Literature

paper_content:

Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx), the studies in identifying genetic variants that may affect drug response, is important for personalized medicine. Computational approaches in studying the relationships between genes and drug response are emerging as an active area of research for personalized medicine. Currently, systematic study of drug-gene relationships is limited because a large-scale machine understandable drug-gene relationship knowledge base is difficult to build and to keep update. Scientific literature contains rich information of drug-gene relationships, therefore is the ultimate knowledge source for PGx studies and for personalized medicine. However, this information is largely buried in free text with limited machine understandability. There is a need to develop automatic approaches to extract structured drug-gene relationships from biomedical literature. In this study, we present a semi-supervised approach to extracting drug-gene relationships from MEDLINE. The technique uses one seed pattern and iteratively learns various ways the relationship may be expressed in 20 million MEDLINE abstracts. Our approach has achieved high precisions (0.961-1.00) in extracting drug-gene relationships from MEDLINE and found many drug-gene pairs that are not available in PharmGKB, a large-scale manually curated PGx knowledge base.
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paper_title: Literature mining on pharmacokinetics numerical data: A feasibility study

paper_content:

A feasibility study of literature mining is conducted on drug PK parameter numerical data with a sequential mining strategy. Firstly, an entity template library is built to retrieve pharmacokinetics relevant articles. Then a set of tagging and extraction rules are applied to retrieve PK data from the article abstracts. To estimate the PK parameter population-average mean and between-study variance, a linear mixed meta-analysis model and an E-M algorithm are developed to describe the probability distributions of PK parameters. Finally, a cross-validation procedure is developed to ascertain false-positive mining results. Using this approach to mine midazolam (MDZ) PK data, an 88% precision rate and 92% recall rate are achieved, with an F-score=90%. It greatly out-performs a conventional data mining approach (support vector machine), which has an F-score of 68.1%. Further investigate on 7 more drugs reveals comparable performances of our sequential mining approach.
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Title: Chemical Named Entities Recognition: A Review on Approaches and Applications

Section 1: Introduction
Description 1: This section introduces the context and significance of chemical named entity recognition, highlighting the challenges and the importance of computational tools.

Section 2: Review
Description 2: This section provides an overview of the paper’s content, organizing different types of chemical entities and the methods used for their recognition.

Section 3: Chemical Literature Mining
Description 3: This section discusses the scope and necessity of mining chemical literature, including examples of the types of information extracted.

Section 4: Sources of Chemical Information
Description 4: This section outlines various sources of chemical information, such as chemical structure and literature databases, and their utility.

Section 5: The Textual Contents of Chemical Documents
Description 5: This section analyzes the contents found in chemical documents and the need for specialized lexicons and systems for identifying chemical entities.

Section 6: Methods for Expressing Chemical Structural Information in Documents
Description 6: This section explores the challenges and methods for representing chemical structures and naming conventions in literature.

Section 7: Evaluation
Description 7: This section introduces the corpora and methodologies for evaluating the performance of chemical named entity recognition tools.

Section 8: Chemical Named Entity Recognition (NER) Approaches
Description 8: This section presents various approaches for NER, including preprocessing steps, feature processing, and name recognition techniques.

Section 9: Dictionary-based NER Systems
Description 9: This section describes systems that use dictionaries for identifying chemical entities, including exact and flexible matching techniques.

Section 10: Rule-based NER Systems
Description 10: This section discusses systems that rely on handcrafted rules for extracting chemical names based on patterns and contextual rules.

Section 11: Machine Learning (ML)-based NER Systems
Description 11: This section covers ML approaches for NER, including supervised, unsupervised, and semi-supervised learning algorithms.

Section 12: Hybrid NER Systems
Description 12: This section looks at systems that combine multiple NER approaches to leverage their strengths and improve performance.

Section 13: Discussion
Description 13: This section provides a comparative discussion of the NER approaches and their relative advantages, challenges, and suitability for chemical named entity recognition.

Section 14: Conclusion
Description 14: This section summarizes the key findings of the paper, emphasizing the importance of advanced NER methods and the potential avenues for future research.