Automatic Keyphrase Extraction: A Survey of the State of the Art
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paper_title: Opinion Expression Mining by Exploiting Keyphrase Extraction

paper_content:

In this paper, we shall introduce a system for extracting the keyphrases for the reason of authors’ opinion from product reviews. The datasets for two fairly different product review domains related to movies and mobile phones were constructed semiautomatically based on the pros and cons entered by the authors. The system illustrates that the classic supervised keyphrase extraction approach – mostly used for scientific genre previously – could be adapted for opinion-related keyphrases. Besides adapting the original framework to this special task through defining novel, taskspecific features, an efficient way of representing keyphrase candidates will be demonstrated as well. The paper also provides a comparison of the effectiveness of the standard keyphrase extraction features and that of the system designed for the special task of opinion expression mining.
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paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: Learning Algorithms for Keyphrase Extraction

paper_content:

Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.
---
paper_title: Automatic Keyphrase Extraction via Topic Decomposition

paper_content:

Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into multiple random walks specific to various topics. We thus build a Topical PageRank (TPR) on word graph to measure word importance with respect to different topics. After that, given the topic distribution of the document, we further calculate the ranking scores of words and extract the top ranked ones as keyphrases. Experimental results show that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
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paper_title: Keyphrase Extraction from Online News Using Binary Integer Programming

paper_content:

In recent years, keyphrase extraction has received great attention, and been successfully employed by various applications. Keyphrases extracted from news articles can be used to concisely represent main contents of news events. Keyphrases can help users to speed up browsing and find the desired contents more quickly. In this paper, we first present several criteria of high-quality news keyphrases. After that, in order to integrate those criteria into the keyphrase extraction task, we propose a novel formulation which converts the task to a binary integer programming problem. The formulation cannot only encode the prior knowledge as constraints, but also learn constraints from data. We evaluate the proposed approach on a manually labeled corpus. Experimental results demonstrate that our approach achieves better performances compared with the state-of-the-art methods.
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paper_title: Topical Keyphrase Extraction from Twitter

paper_content:

Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
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paper_title: A Language Model Approach To Keyphrase Extraction

paper_content:

We present a new approach to extracting keyphrases based on statistical language models. Our approach is to use pointwise KL-divergence between multiple language models for scoring both phraseness and informativeness, which can be unified into a single score to rank extracted phrases.
---
paper_title: Finding advertising keywords on web pages

paper_content:

A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of revenue supporting the web today. Despite the importance of this area, little formal, published research exists. We describe a system that learns how to extract keywords from web pages for advertisement targeting. The system uses a number of features, such as term frequency of each potential keyword, inverse document frequency, presence in meta-data, and how often the term occurs in search query logs. The system is trained with a set of example pages that have been hand-labeled with "relevant" keywords. Based on this training, it can then extract new keywords from previously unseen pages. Accuracy is substantially better than several baseline systems.
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paper_title: Extracting Keywords from Multi-party Live Chats

paper_content:

Live chats have become a popular form of communication, connecting people all over the globe. We believe that one of the simplest approaches for providing topic information to users joining a chat is keywords. In this paper, we present a method to automatically extract contextually relevant keywords for multi-party live chats. In our work, we identify keywords that are associated with specific dialogue acts as well as the occurrences of keywords across the entire conversation. In this way, we are able to identify distinguishing features of the chat based on structural information derived from live chats and predicted dialogue acts. In evaluation, we find that using structural information and predicted dialogue acts performs well, and that conventional methods do not work well over live chats.
---
paper_title: Coherent Keyphrase Extraction via Web Mining

paper_content:

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).
---
paper_title: TextRank: Bringing Order Into Texts

paper_content:

In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
---
paper_title: Human-competitive tagging using automatic keyphrase extraction

paper_content:

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
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paper_title: Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art

paper_content:

State-of-the-art approaches for unsupervised keyphrase extraction are typically evaluated on a single dataset with a single parameter setting. Consequently, it is unclear how effective these approaches are on a new dataset from a different domain, and how sensitive they are to changes in parameter settings. To gain a better understanding of state-of-the-art unsupervised keyphrase extraction algorithms, we conduct a systematic evaluation and analysis of these algorithms on a variety of standard evaluation datasets.
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paper_title: Single document keyphrase extraction using neighborhood knowledge

paper_content:

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.
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paper_title: Keyphrase Extraction Using Semantic Networks Structure Analysis

paper_content:

Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network are used to extract keyphrases (key nodes) unsupervised. Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%.
---
paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: Extracting key terms from noisy and multitheme documents

paper_content:

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
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paper_title: Domain-specific keyphrase extraction

paper_content:

Document keyphrases provide semantic metadata characterizing documents and producing an overview of the content of a document. They can be used in many text-mining and knowledge management related applications. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified domain keyphrases to assign weights to the candidate keyphrases. The logic of our algorithm is: the more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. To obtain prior positive inputs, KIP first populates its glossary database using manually identified keyphrases and keywords. It then checks the composition of all noun phrases of a document, looks up the database and calculates scores for all these noun phrases. The ones having higher scores will be extracted as keyphrases.
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paper_title: Improved Automatic Keyword Extraction Given More Linguistic Knowledge

paper_content:

In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
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paper_title: Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts

paper_content:

This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part-of-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a word based on its connection with other sentences or words. The system performance is evaluated in different ways, including comparison to human annotated keywords using F-measure and a weighted score relative to the oracle system performance, as well as a novel alternative human evaluation. Our results have shown that the simple unsupervised TFIDF approach performs reasonably well, and the additional information from POS and sentence score helps keyword extraction. However, the graph method is less effective for this domain. Experiments were also performed using speech recognition output and we observed degradation and different patterns compared to human transcripts.
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paper_title: TextRank: Bringing Order Into Texts

paper_content:

In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
---
paper_title: Using Noun Phrase Heads to Extract Document Keyphrases

paper_content:

Automatically extracting keyphrases from documents is a task with many applications in information retrieval and natural language processing. Document retrieval can be biased towards documents containing relevant keyphrases; documents can be classified or categorized based on their keyphrases; automatic text summarization may extract sentences with high keyphrase scores. ::: ::: This paper describes a simple system for choosing noun phrases from a document as keyphrases. A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary. ::: ::: Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.
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paper_title: An Ontology-Based Approach for Key Phrase Extraction

paper_content:

Automatic key phrase extraction is fundamental to the success of many recent digital library applications and semantic information retrieval techniques and a difficult and essential problem in Vietnamese natural language processing (NLP). In this work, we propose a novel method for key phrase extracting of Vietnamese text that exploits the Vietnamese Wikipedia as an ontology and exploits specific characteristics of the Vietnamese language for the key phrase selection stage. We also explore NLP techniques that we propose for the analysis of Vietnamese texts, focusing on the advanced candidate phrases recognition phase as well as part-of-speech (POS) tagging. Finally, we review the results of several experiments that have examined the impacts of strategies chosen for Vietnamese key phrase extracting.
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paper_title: Bayesian Text Segmentation for Index Term Identification and Keyphrase Extraction

paper_content:

Automatically extracting terminology and index terms from scientific literature is useful for a variety of digital library, indexing and search applications. This task is non-trivial, compli- cated by domain-specific terminology and a steady introduction of new terminology. Correctly identifying nested terminology further adds to the challenge. We present a Dirichlet Process (DP) model of word segmentation where multiword segments are either retrieved from a cache or newly generated. We show how this DP-Segmentation model can be used to successfully extract nested terminology, outperforming previous methods for solving this problem.
---
paper_title: Human-competitive tagging using automatic keyphrase extraction

paper_content:

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
---
paper_title: KP-Miner: A keyphrase extraction system for English and Arabic documents

paper_content:

Automatic keyphrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with widely used systems that it is effective and efficient in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system does not need to be trained on a particular document set in order to achieve its task. It also has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrases. This implies that the users of this system can use their understanding of the document(s) being input into the system to fine-tune it to their particular needs.
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paper_title: Automatic keyphrase extraction from scientific documents using N-gram filtration technique

paper_content:

In this paper we present an automatic Keyphrase extraction technique for English documents of scientific domain. The devised algorithm uses n-gram filtration technique, which filters sophisticated n-grams {1dnd4} along with their weight from the words of input document. To develop n-gram filtration technique, we have used (1) LZ78 data compression based technique, (2) a simple refinement step, (3) A simple Pattern Filtration algorithm and, (4) a term weighting scheme. In term weighting scheme, we have introduced the importance of position of sentence (where given phrase occurs first) in document and position of phrase in sentence for documents of scientific domain (which is literally more organized than other domains). The entire system is based upon statistical observations, simple grammatical facts, heuristics, and lexical information of English language. We remark that the devised system does not require a learning phase. Our experimental results with publically available text dataset, shows that the devised system is comparable with other known algorithms.
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paper_title: Automatic Keyphrase Extraction with a Refined Candidate Set

paper_content:

In this paper, we develop and evaluate an automatic keyphrase extraction technique for scientific documents. A new candidate phrase generation method is proposed based on the core word expansion algorithm, which can reduce the size of candidate set by about 75% without increasing the computational complexity. Then in the step of feature calculation, when a phrase and its sub-phrases coexist as candidates, an inverse document frequency related feature is introduced for selecting the proper granularity. Experimental results show the efficiency and effectiveness of the refined candidate set and demonstrate that the overall performance of our system compares favorably with other known keyphrase extraction systems.
---
paper_title: Finding advertising keywords on web pages

paper_content:

A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of revenue supporting the web today. Despite the importance of this area, little formal, published research exists. We describe a system that learns how to extract keywords from web pages for advertisement targeting. The system uses a number of features, such as term frequency of each potential keyword, inverse document frequency, presence in meta-data, and how often the term occurs in search query logs. The system is trained with a set of example pages that have been hand-labeled with "relevant" keywords. Based on this training, it can then extract new keywords from previously unseen pages. Accuracy is substantially better than several baseline systems.
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paper_title: CoRankBayes: bayesian learning to rank under the co-training framework and its application in keyphrase extraction

paper_content:

Recently, learning to rank algorithms have become a popular and effective tool for ordering objects (e.g. terms) according to their degrees of importance. The contribution of this paper is that we propose a simple and fast learning to rank model RankBayes and embed it in the co-training framework. The detailed proof is given that Naive Bayes algorithm can be used to implement a learning to rank model. To solve the problem of two-model inconsistency, an ingenious approach is put forward to rank all the phrases by making use of the labeled results of two RankBayes models. Experimental results show that the proposed approach is promising in solving ranking problems.
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paper_title: KPSpotter: a flexible information gain-based keyphrase extraction system

paper_content:

To tackle the issue of information overload, we present an Information Gain-based KeyPhrase Extraction System, called KPSpotter. KPSpotter is a flexible web-enabled keyphrase extraction system, capable of processing various formats of input data, including web data, and generating the extraction model as well as the list of keyphrases in XML. In KPSpotter, the following two features were selected for training and extracting keyphrases: 1) TF*IDF and 2) Distance from First Occurrence. Input training and testing collections were processed in three stages: 1) Data Cleaning, 2) Data Tokenizing, and 3) Data Discretizing. To measure the system performance, the keyphrases extracted by KPSpotter are compared with the ones that the authors assigned. Our experiments show that the performance of KPSpotter was evaluated to be equivalent to KEA, a well-known keyphrase extraction system. KPSpotter, however, is differentiated from other extraction systems in the followings: First, KPSpotter employs a new keyphrase extraction technique that combines the Information Gain data mining measure and several Natural Language Processing techniques such as stemming and case-folding. Second, KPSpotter is able to process various types of input data such as XML, HTML, and unstructured text data and generate XML output. Third, the user can provide input data and execute KPSpotter through the Internet. Fourth, for efficiency and performance reason, KPSpotter stores candidate keyphrases and its related information such as frequency and stemmed form into an embedded database management system.
---
paper_title: Improved Automatic Keyword Extraction Given More Linguistic Knowledge

paper_content:

In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
---
paper_title: Learning Algorithms for Keyphrase Extraction

paper_content:

Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.
---
paper_title: HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID

paper_content:

The Semeval task 5 was an opportunity for experimenting with the key term extraction module of GROBID, a system for extracting and generating bibliographical information from technical and scientific documents. The tool first uses GROBID's facilities for analyzing the structure of scientific articles, resulting in a first set of structural features. A second set of features captures content properties based on phraseness, informativeness and keywordness measures. Two knowledge bases, GRISP and Wikipedia, are then exploited for producing a last set of lexical/semantic features. Bagged decision trees appeared to be the most efficient machine learning algorithm for generating a list of ranked key term candidates. Finally a post ranking was realized based on statistics of cousage of keywords in HAL, a large Open Access publication repository.
---
paper_title: A ranking approach to keyphrase extraction

paper_content:

This paper addresses the issue of automatically extracting keyphrases from a document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the problem as ranking and employ a learning to rank method to perform the task. Specifically, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.
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paper_title: Enhancing Linguistically Oriented Automatic Keyword Extraction

paper_content:

This paper presents experiments on how the performance of automatic keyword extraction can be improved, as measured by keywords previously assigned by professional indexers. The keyword extraction algorithm consists of three prediction models that are combined to decide what words or sequences of words in the documents are suitable as keywords. The models, in turn, are built using different definitions of what constitutes a term in a written document.
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paper_title: Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles

paper_content:

We tackle two major issues in automatic keyphrase extraction using scientific articles: candidate selection and feature engineering. To develop an efficient candidate selection method, we analyze the nature and variation of keyphrases and then select candidates using regular expressions. Secondly, we re-examine the existing features broadly used for the supervised approach, exploring different ways to enhance their performance. While most other approaches are supervised, we also study the optimal features for unsupervised keyphrase extraction. Our research has shown that effective candidate selection leads to better performance as evaluation accounts for candidate coverage. Our work also attests that many of existing features are also usable in unsupervised extraction.
---
paper_title: Finding advertising keywords on web pages

paper_content:

A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of revenue supporting the web today. Despite the importance of this area, little formal, published research exists. We describe a system that learns how to extract keywords from web pages for advertisement targeting. The system uses a number of features, such as term frequency of each potential keyword, inverse document frequency, presence in meta-data, and how often the term occurs in search query logs. The system is trained with a set of example pages that have been hand-labeled with "relevant" keywords. Based on this training, it can then extract new keywords from previously unseen pages. Accuracy is substantially better than several baseline systems.
---
paper_title: Re-examining Automatic Keyphrase Extraction Approaches in Scientific Articles

paper_content:

We tackle two major issues in automatic keyphrase extraction using scientific articles: candidate selection and feature engineering. To develop an efficient candidate selection method, we analyze the nature and variation of keyphrases and then select candidates using regular expressions. Secondly, we re-examine the existing features broadly used for the supervised approach, exploring different ways to enhance their performance. While most other approaches are supervised, we also study the optimal features for unsupervised keyphrase extraction. Our research has shown that effective candidate selection leads to better performance as evaluation accounts for candidate coverage. Our work also attests that many of existing features are also usable in unsupervised extraction.
---
paper_title: Finding advertising keywords on web pages

paper_content:

A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of revenue supporting the web today. Despite the importance of this area, little formal, published research exists. We describe a system that learns how to extract keywords from web pages for advertisement targeting. The system uses a number of features, such as term frequency of each potential keyword, inverse document frequency, presence in meta-data, and how often the term occurs in search query logs. The system is trained with a set of example pages that have been hand-labeled with "relevant" keywords. Based on this training, it can then extract new keywords from previously unseen pages. Accuracy is substantially better than several baseline systems.
---
paper_title: Coherent Keyphrase Extraction via Web Mining

paper_content:

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).
---
paper_title: HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID

paper_content:

The Semeval task 5 was an opportunity for experimenting with the key term extraction module of GROBID, a system for extracting and generating bibliographical information from technical and scientific documents. The tool first uses GROBID's facilities for analyzing the structure of scientific articles, resulting in a first set of structural features. A second set of features captures content properties based on phraseness, informativeness and keywordness measures. Two knowledge bases, GRISP and Wikipedia, are then exploited for producing a last set of lexical/semantic features. Bagged decision trees appeared to be the most efficient machine learning algorithm for generating a list of ranked key term candidates. Finally a post ranking was realized based on statistics of cousage of keywords in HAL, a large Open Access publication repository.
---
paper_title: Human-competitive tagging using automatic keyphrase extraction

paper_content:

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
---
paper_title: Single document keyphrase extraction using neighborhood knowledge

paper_content:

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.
---
paper_title: Extracting key terms from noisy and multitheme documents

paper_content:

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
---
paper_title: CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction

paper_content:

Previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are considered independent of each other. This paper proposes a novel approach named CollabRank to collaborative single-document keyphrase extraction by making use of mutual influences of multiple documents within a cluster context. CollabRank is implemented by first employing the clustering algorithm to obtain appropriate document clusters, and then using the graph-based ranking algorithm for collaborative single-document keyphrase extraction within each cluster. Experimental results demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the system performance relies positively on the quality of document clusters.
---
paper_title: Keyword extraction from a single document using word co-occurrence statistical information

paper_content:

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of cooccurrence between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the documentas follows. If probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of biases of distribution is measured by the χ 2 -measure. Our algorithm shows comparable performance to tfidf without using a corpus.
---
paper_title: TextRank: Bringing Order Into Texts

paper_content:

In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
---
paper_title: The Anatomy of a Large-Scale Hypertextual Web Search Engine

paper_content:

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from 3years ago. This paper provides an in-depth description of our large-scale web search engine - the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections, where anyone can publish anything they want.
---
paper_title: TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction

paper_content:

Keyphrase extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper we present TopicRank, a graph-based keyphrase extraction method that relies on a topical representation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-based ranking model is applied to assign a significance score to each topic. Keyphrases are then generated by selecting a candidate from each of the topranked topics. We conducted experiments on four evaluation datasets of different languages and domains. Results show that TopicRank significantly outperforms state-of-the-art methods on three datasets.
---
paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: Extracting key terms from noisy and multitheme documents

paper_content:

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
---
paper_title: Topical Word Trigger Model for Keyphrase Extraction

paper_content:

Keyphrase extraction aims to find representative phrases for a document. Keyphrases are expected to cover main themes of a document. Meanwhile, keyphrases do not necessarily occur frequently in the document, which is known as the vocabulary gap between the words in a document and its keyphrases. In this paper, we propose Topical Word Trigger Model (TWTM) for keyphrase extraction. TWTM assumes the content and keyphrases of a document are talking about the same themes but written in different languages. Under the assumption, keyphrase extraction is modeled as a translation process from document contenttokeyphrases. Moreover,in ordertobettercoverdocumentthemes, TWTMsets trigger probabilities to be topic-specific, and hence the trigger process can be influenced by the document themes. On one hand, TWTM uses latent topics to model document themes and takes the coverage of document themes into consideration; on the other hand, TWTM uses topic-specific word trigger to bridge the vocabulary gap between the words in document and keyphrases. Experiment results on real world dataset reveal that TWTM outperforms existing state-of-the-art methods under various evaluation metrics.
---
paper_title: Improved Automatic Keyword Extraction Given More Linguistic Knowledge

paper_content:

In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
---
paper_title: Automatic Keyphrase Extraction via Topic Decomposition

paper_content:

Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into multiple random walks specific to various topics. We thus build a Topical PageRank (TPR) on word graph to measure word importance with respect to different topics. After that, given the topic distribution of the document, we further calculate the ranking scores of words and extract the top ranked ones as keyphrases. Experimental results show that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
---
paper_title: Latent Dirichlet Allocation

paper_content:

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.
---
paper_title: Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering

paper_content:

A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents.
---
paper_title: Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction

paper_content:

Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words, either homogeneous or heterogeneous. Experimental results show the effectiveness of the proposed approach for both tasks. The corpus-based approach is validated to work almost as well as the knowledge-based approach for computing word semantics.
---
paper_title: A Language Model Approach To Keyphrase Extraction

paper_content:

We present a new approach to extracting keyphrases based on statistical language models. Our approach is to use pointwise KL-divergence between multiple language models for scoring both phraseness and informativeness, which can be unified into a single score to rank extracted phrases.
---
paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: Approximate Matching for Evaluating Keyphrase Extraction

paper_content:

We propose a new evaluation strategy for keyphrase extraction based on approximate keyphrase matching. It corresponds well with human judgments and is better suited to assess the performance of keyphrase extraction approaches. Additionally, we propose a generalized framework for comprehensive analysis of keyphrase extraction that subsumes most existing approaches, which allows for fair testing conditions. For the first time, we compare the results of state-of-the-art unsupervised and supervised keyphrase extraction approaches on three evaluation datasets and show that the relative performance of the approaches heavily depends on the evaluation metric as well as on the properties of the evaluation dataset.
---
paper_title: Keyword extraction from a single document using word co-occurrence statistical information

paper_content:

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of cooccurrence between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the documentas follows. If probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of biases of distribution is measured by the χ 2 -measure. Our algorithm shows comparable performance to tfidf without using a corpus.
---
paper_title: TextRank: Bringing Order Into Texts

paper_content:

In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
---
paper_title: Automatic Keyphrase Extraction via Topic Decomposition

paper_content:

Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents and words can be represented by a mixture of semantic topics, we propose to decompose traditional random walk into multiple random walks specific to various topics. We thus build a Topical PageRank (TPR) on word graph to measure word importance with respect to different topics. After that, given the topic distribution of the document, we further calculate the ranking scores of words and extract the top ranked ones as keyphrases. Experimental results show that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
---
paper_title: KP-Miner: A keyphrase extraction system for English and Arabic documents

paper_content:

Automatic keyphrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with widely used systems that it is effective and efficient in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system does not need to be trained on a particular document set in order to achieve its task. It also has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrases. This implies that the users of this system can use their understanding of the document(s) being input into the system to fine-tune it to their particular needs.
---
paper_title: Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction

paper_content:

This paper describes a feasibility study of n-gram-based evaluation metrics for automatic keyphrase extraction. To account for near-misses currently ignored by standard evaluation metrics, we adapt various evaluation metrics developed for machine translation and summarization, and also the R-precision evaluation metric from keyphrase evaluation. In evaluation, the R-precision metric is found to achieve the highest correlation with human annotations. We also provide evidence that the degree of semantic similarity varies with the location of the partially-matching component words.
---
paper_title: Single document keyphrase extraction using neighborhood knowledge

paper_content:

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.
---
paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles

paper_content:

This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.
---
paper_title: Improved Automatic Keyword Extraction Given More Linguistic Knowledge

paper_content:

In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
---
paper_title: Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts

paper_content:

This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated part-of-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a word based on its connection with other sentences or words. The system performance is evaluated in different ways, including comparison to human annotated keywords using F-measure and a weighted score relative to the oracle system performance, as well as a novel alternative human evaluation. Our results have shown that the simple unsupervised TFIDF approach performs reasonably well, and the additional information from POS and sentence score helps keyword extraction. However, the graph method is less effective for this domain. Experiments were also performed using speech recognition output and we observed degradation and different patterns compared to human transcripts.
---
paper_title: Human-competitive tagging using automatic keyphrase extraction

paper_content:

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
---
paper_title: Automatic Keyphrase Extraction by Bridging Vocabulary Gap

paper_content:

Keyphrase extraction aims to select a set of terms from a document as a short summary of the document. Most methods extract keyphrases according to their statistical properties in the given document. Appropriate keyphrases, however, are not always statistically significant or even do not appear in the given document. This makes a large vocabulary gap between a document and its keyphrases. In this paper, we consider that a document and its keyphrases both describe the same object but are written in two different languages. By regarding keyphrase extraction as a problem of translating from the language of documents to the language of keyphrases, we use word alignment models in statistical machine translation to learn translation probabilities between the words in documents and the words in keyphrases. According to the translation model, we suggest keyphrases given a new document. The suggested keyphrases are not necessarily statistically frequent in the document, which indicates that our method is more flexible and reliable. Experiments on news articles demonstrate that our method outperforms existing unsupervised methods on precision, recall and F-measure.
---
paper_title: Clustering to Find Exemplar Terms for Keyphrase Extraction

paper_content:

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
---
paper_title: Extracting key terms from noisy and multitheme documents

paper_content:

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
---
paper_title: Freebase: a collaboratively created graph database for structuring human knowledge

paper_content:

Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
---
paper_title: Human-competitive tagging using automatic keyphrase extraction

paper_content:

This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folksonomy using traditional evaluation techniques. Next, we demonstrate how documents can be tagged automatically with a state-of-the-art keyphrase extraction algorithm, and further improve performance in this new domain using a new algorithm, "Maui", that utilizes semantic information extracted from Wikipedia. Maui outperforms existing approaches and extracts tags that are competitive with those assigned by the best performing human taggers.
---

Title: Automatic Keyphrase Extraction: A Survey of the State of the Art

Section 1: Introduction
Description 1: This section provides an overview and the importance of automatic keyphrase extraction, highlighting its applications and the challenges faced by current systems.

Section 2: Corpora
Description 2: This section discusses the different types of corpora used for evaluating keyphrase extraction systems, along with factors that affect extraction difficulty.

Section 3: Keyphrase Extraction Approaches
Description 3: This section outlines the general steps involved in keyphrase extraction systems and categorizes the approaches into supervised and unsupervised methods.

Section 4: Selecting Candidate Words and Phrases
Description 4: This section details the heuristics and rules used to extract candidate keyphrases from documents.

Section 5: Supervised Approaches
Description 5: This section covers the task reformulation and feature design in supervised keyphrase extraction systems.

Section 6: Unsupervised Approaches
Description 6: This section categorizes and describes different unsupervised methods for keyphrase extraction, including graph-based ranking, topic-based clustering, simultaneous learning, and language modeling.

Section 7: Evaluation
Description 7: This section discusses the metrics and methods used for evaluating keyphrase extraction systems and presents state-of-the-art results on commonly-used datasets.

Section 8: Analysis
Description 8: This section provides an error analysis of state-of-the-art keyphrase extraction systems, identifying common types of errors and their contributions to overall performance.

Section 9: Recommendations
Description 9: This section offers recommendations for improving keyphrase extraction by incorporating background knowledge and addressing identified error types.

Section 10: Conclusion and Future Directions
Description 10: This section summarizes the survey findings and outlines the major challenges and future directions for research in automatic keyphrase extraction.