A survey of automatic term extraction for Brazilian Portuguese
---
paper_title: Methods of Automatic Term Recognition - A Review

paper_content:

Following the growing interest in "corpus-based" approaches to computational linguistics, a number of studies have recently appeared on the topic of automatic term recognition or extraction. Because a successful term-recognition method has to be based on proper insights into the nature of terms, studies of automatic term recognition not only contribute to the applications of computational linguistics but also to the theoretical foundation of terminology. Many studies on automatic term recognition treat interesting aspects of terms, but most of them are not well founded and described.This paper tries to give an overview of the principles and methods of automatic term recognition. For that purpose, two major trends are examined, i.e., studies in automatic recognition of significant elements for indexing mainly carried out in information-retrieval circles and current research in automatic term recognition in the field of computational linguistics.
---
paper_title: EχATOLP – An Automatic Tool for Term Extraction from Portuguese Language Corpora

paper_content:

This paper describes EχATOLP, a software tool to extract significant terms from an annotated corpus written in portuguese about a specific domain of interest. Being based on linguistic and statistical approaches, this tool extracts terms that are frequent and syntactic relevant to the domain of interest.
---
paper_title: Reviewing and Evaluating Automatic Term Recognition Techniques

paper_content:

Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-basedapproaches measure the degree that a candidate term refers to a domain specific concept. Unithood-basedapproaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATRmethods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATRmethods, showing that termhood-basedmethods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATRperformance.
---
paper_title: Applying transductive learning for automatic term extraction: The case of the ecology domain

paper_content:

Terms are the basis for general text mining and natural language processing applications. However, the manual term extraction is unfeasible due to the huge number of words presented in a domain corpus and also the human effort required to do the extraction. For the term extraction task, machine learning techniques have been used to perform automatic term extraction (ATE). Inductive learning is commonly used, but it requires a large number of words labeled as terms and non-terms to build a classification model and classify unseen words. A better solution is the use of transductive learning, since it requires a small number of labeled examples to classify words as terms and non-terms. In this paper, we propose the use of transductive learning to ATE. The obtained results demonstrate that the application of transductive learning to ATE produces better results than the results obtained by inductive learning.
---
paper_title: A statistical approach to term extraction

paper_content:

This paper argues in favor of a statistical approach to terminology extraction, general to all languages but with language specific parameters. In contrast to many application-oriented terminology studies, which are focused on a particular language and domain, this paper adopts some general principles of the statistical properties of terms and a method to obtain the corresponding language specific parameters. This method is used for the automatic identification of terminology and is quantitatively evaluated in an empirical study of English medical terms. The proposal is theoretically and computationally simple and disregards resources such as linguistic or ontological knowledge. The algorithm learns to identify terms during a training phase where it is shown examples of both terminological and non-terminological units. With these examples, the algorithm creates a model of the terminology that accounts for the frequency of lexical, morphological and syntactic elements of the terms in relation to the non-terminological vocabulary. The model is then used for the later identification of new terminology in previously unseen text. The comparative evaluation shows that performance is significantly higher than other well-known systems.
---
paper_title: A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set

paper_content:

In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
---
paper_title: Improving portuguese term extraction

paper_content:

This paper presents the evaluation of a set of heuristics to improve the quality of extracted terms from an annotated domain corpus written in Portuguese. The proposed heuristics start from part-of-speech and grammatical functional annotation of texts, identifying nouns and noun phrases that are the best candidates to be considered terms of the domain. These nouns and noun phrases are submitted to a set of approximative rules (heuristics) that may either discard some, accept others (removing words or not), or even discover implicit terms that can be inferred. The effectiveness of these heuristics is verified through a corpus experiment, on the basis of a reference list for which usual metrics are computed.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: Applying transductive learning for automatic term extraction: The case of the ecology domain

paper_content:

Terms are the basis for general text mining and natural language processing applications. However, the manual term extraction is unfeasible due to the huge number of words presented in a domain corpus and also the human effort required to do the extraction. For the term extraction task, machine learning techniques have been used to perform automatic term extraction (ATE). Inductive learning is commonly used, but it requires a large number of words labeled as terms and non-terms to build a classification model and classify unseen words. A better solution is the use of transductive learning, since it requires a small number of labeled examples to classify words as terms and non-terms. In this paper, we propose the use of transductive learning to ATE. The obtained results demonstrate that the application of transductive learning to ATE produces better results than the results obtained by inductive learning.
---
paper_title: A Practical Course In Terminology Processing

paper_content:

Since the advent of the computer, terminology management can be carried out by almost anyone who has learnt to use a computer. Terminology management has proved to be an efficient tool in international communications in industry, education and international organisations. Software packages are readily available and international corporations often have their own terminology database. Following these developments, translators and terminologists are confronted with a specialised form of information management involving compilation and standardisation of vocabulary, storage, retrieval and updating. A Practical Course in Terminology Processing provides the key to methods of terminology management for the English language, for general and specific purposes. This unique course has been developed on the basis of years of teaching experience and research at the University of Manchester Institute of Science and Technology (UMIST, UK) and is particularly suitable for translation courses, freelance translators, technical writers, as well as for non-linguists who are confronted with terminology processing as part of their profession. The 1996 reprint of the paperback edition includes an index.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: Methods of Automatic Term Recognition - A Review

paper_content:

Following the growing interest in "corpus-based" approaches to computational linguistics, a number of studies have recently appeared on the topic of automatic term recognition or extraction. Because a successful term-recognition method has to be based on proper insights into the nature of terms, studies of automatic term recognition not only contribute to the applications of computational linguistics but also to the theoretical foundation of terminology. Many studies on automatic term recognition treat interesting aspects of terms, but most of them are not well founded and described.This paper tries to give an overview of the principles and methods of automatic term recognition. For that purpose, two major trends are examined, i.e., studies in automatic recognition of significant elements for indexing mainly carried out in information-retrieval circles and current research in automatic term recognition in the field of computational linguistics.
---
paper_title: Improving portuguese term extraction

paper_content:

This paper presents the evaluation of a set of heuristics to improve the quality of extracted terms from an annotated domain corpus written in Portuguese. The proposed heuristics start from part-of-speech and grammatical functional annotation of texts, identifying nouns and noun phrases that are the best candidates to be considered terms of the domain. These nouns and noun phrases are submitted to a set of approximative rules (heuristics) that may either discard some, accept others (removing words or not), or even discover implicit terms that can be inferred. The effectiveness of these heuristics is verified through a corpus experiment, on the basis of a reference list for which usual metrics are computed.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: Reviewing and Evaluating Automatic Term Recognition Techniques

paper_content:

Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-basedapproaches measure the degree that a candidate term refers to a domain specific concept. Unithood-basedapproaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATRmethods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATRmethods, showing that termhood-basedmethods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATRperformance.
---
paper_title: Managing Gigabytes : Compressing and Indexing documents and images

paper_content:

This book published in 1999 is a revised second edition of the version that first appeared in 1994. The authors have incorporated many changes in the second edition by updating various chapters to include the latest developments. The book is concerned with the task of managing large volumes of text and image data amounting to several gigabytes. The fundamental problems addressed in the book include compressing such data and indexing it, in order to enable easy search. Many books look at compressing and indexing as if they are unrelated techniques; however, this book brings out the advantages of combining them in a beneficial way.
---
paper_title: An Evaluation on Feature Selection for Text Clustering

paper_content:

Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called "Term Contribution (TC)" and perform a comparative study on a variety of feature selection methods for text clustering, including Document Frequency (DF), Term Strength (TS), Entropy-based (En), Information Gain (IG) and χ2 statistic (CHI). Finally, we propose an "Iterative Feature Selection (IF)" method that addresses the unavailability of label problem by utilizing effective supervised feature selection method to iteratively select features and perform clustering. Detailed experimental results on Web Directory data are provided in the paper.
---
paper_title: Applying transductive learning for automatic term extraction: The case of the ecology domain

paper_content:

Terms are the basis for general text mining and natural language processing applications. However, the manual term extraction is unfeasible due to the huge number of words presented in a domain corpus and also the human effort required to do the extraction. For the term extraction task, machine learning techniques have been used to perform automatic term extraction (ATE). Inductive learning is commonly used, but it requires a large number of words labeled as terms and non-terms to build a classification model and classify unseen words. A better solution is the use of transductive learning, since it requires a small number of labeled examples to classify words as terms and non-terms. In this paper, we propose the use of transductive learning to ATE. The obtained results demonstrate that the application of transductive learning to ATE produces better results than the results obtained by inductive learning.
---
paper_title: A comparative study on unsupervised feature selection methods for text clustering

paper_content:

Text clustering is one of the central problems in text mining and information retrieval area. For the high dimensionality of feature space and the inherent data sparsity, performance of clustering algorithms will dramatically decline. Two techniques are used to deal with this problem: feature extraction and feature selection. Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, four unsupervised feature selection methods, DF, TC, TVQ, and a new proposed method TV are introduced. Experiments are taken to show that feature selection methods can improves efficiency as well as accuracy of text clustering. Three clustering validity criterions are studied and used to evaluate clustering results.
---
paper_title: A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set

paper_content:

In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
---
paper_title: Improving portuguese term extraction

paper_content:

This paper presents the evaluation of a set of heuristics to improve the quality of extracted terms from an annotated domain corpus written in Portuguese. The proposed heuristics start from part-of-speech and grammatical functional annotation of texts, identifying nouns and noun phrases that are the best candidates to be considered terms of the domain. These nouns and noun phrases are submitted to a set of approximative rules (heuristics) that may either discard some, accept others (removing words or not), or even discover implicit terms that can be inferred. The effectiveness of these heuristics is verified through a corpus experiment, on the basis of a reference list for which usual metrics are computed.
---
paper_title: Tools for nominalization: an alternative for lexical normalization

paper_content:

The recognition of morphological variation and conceptual proximity of the words is crucial for tasks where the lexical normalization is used, such as term generation and matching in an information retrieval environment. We present tools that automatically perform nominalization for lexical normalization in Portuguese. Comparing the effects of three alternative strategies (stemming, lemmatizing, and our proposal: nominalization), we demonstrate through an experimental evaluation that nominalization, as lexical normalization, contributes to the performance improvement in a probabilistic information retrieval approach for Portuguese.
---
paper_title: A statistical approach to term extraction

paper_content:

This paper argues in favor of a statistical approach to terminology extraction, general to all languages but with language specific parameters. In contrast to many application-oriented terminology studies, which are focused on a particular language and domain, this paper adopts some general principles of the statistical properties of terms and a method to obtain the corresponding language specific parameters. This method is used for the automatic identification of terminology and is quantitatively evaluated in an empirical study of English medical terms. The proposal is theoretically and computationally simple and disregards resources such as linguistic or ontological knowledge. The algorithm learns to identify terms during a training phase where it is shown examples of both terminological and non-terminological units. With these examples, the algorithm creates a model of the terminology that accounts for the frequency of lexical, morphological and syntactic elements of the terms in relation to the non-terminological vocabulary. The model is then used for the later identification of new terminology in previously unseen text. The comparative evaluation shows that performance is significantly higher than other well-known systems.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: The C-value/NC-value Method of Automatic Recognition for Multi-Word Terms

paper_content:

Technical terms (henceforth called simply terms), are important elements for digital libraries. In this paper we present a domain-independent method for the automatic extraction of multi-word terms, from machine-readable special language corpora. ::: ::: The method, (C-value/NC-value), combines linguistic and statistical information. The first part, C-value enhances the common statistical measure of frequency of occurrence for term extraction, making it sensitive to a particular type of multi-word terms, the nested terms. The second part, NC-value, gives: 1) a method for the extraction of term context words (words that tend to appear with terms), 2) the incorporation of information from term context words to the extraction of terms.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: The C-value/NC-value Method of Automatic Recognition for Multi-Word Terms

paper_content:

Technical terms (henceforth called simply terms), are important elements for digital libraries. In this paper we present a domain-independent method for the automatic extraction of multi-word terms, from machine-readable special language corpora. ::: ::: The method, (C-value/NC-value), combines linguistic and statistical information. The first part, C-value enhances the common statistical measure of frequency of occurrence for term extraction, making it sensitive to a particular type of multi-word terms, the nested terms. The second part, NC-value, gives: 1) a method for the extraction of term context words (words that tend to appear with terms), 2) the incorporation of information from term context words to the extraction of terms.
---
paper_title: A Practical Course In Terminology Processing

paper_content:

Since the advent of the computer, terminology management can be carried out by almost anyone who has learnt to use a computer. Terminology management has proved to be an efficient tool in international communications in industry, education and international organisations. Software packages are readily available and international corporations often have their own terminology database. Following these developments, translators and terminologists are confronted with a specialised form of information management involving compilation and standardisation of vocabulary, storage, retrieval and updating. A Practical Course in Terminology Processing provides the key to methods of terminology management for the English language, for general and specific purposes. This unique course has been developed on the basis of years of teaching experience and research at the University of Manchester Institute of Science and Technology (UMIST, UK) and is particularly suitable for translation courses, freelance translators, technical writers, as well as for non-linguists who are confronted with terminology processing as part of their profession. The 1996 reprint of the paperback edition includes an index.
---
paper_title: Methods of Automatic Term Recognition - A Review

paper_content:

Following the growing interest in "corpus-based" approaches to computational linguistics, a number of studies have recently appeared on the topic of automatic term recognition or extraction. Because a successful term-recognition method has to be based on proper insights into the nature of terms, studies of automatic term recognition not only contribute to the applications of computational linguistics but also to the theoretical foundation of terminology. Many studies on automatic term recognition treat interesting aspects of terms, but most of them are not well founded and described.This paper tries to give an overview of the principles and methods of automatic term recognition. For that purpose, two major trends are examined, i.e., studies in automatic recognition of significant elements for indexing mainly carried out in information-retrieval circles and current research in automatic term recognition in the field of computational linguistics.
---
paper_title: EχATOLP – An Automatic Tool for Term Extraction from Portuguese Language Corpora

paper_content:

This paper describes EχATOLP, a software tool to extract significant terms from an annotated corpus written in portuguese about a specific domain of interest. Being based on linguistic and statistical approaches, this tool extracts terms that are frequent and syntactic relevant to the domain of interest.
---
paper_title: CSTNews - A Discourse-Annotated Corpus for Single and Multi-Document Summarization of News Texts in Brazilian Portuguese

paper_content:

Summary. This paper introduces CSTNews, a discourse-annotated corpus for fostering research on single and multi-document summarization. The corpus comprises 50 clusters of news texts in Brazilian Portuguese and some related material, which includes a set of single-document manual summaries and a set of multi-document manual and automatic summaries. The texts are annotated in different ways for discourse organization, following both the Rhetorical Structure Theory and Cross-document Structure Theory. The corpus is a result delivered within the context of the SUCINTO Project, which aims at investigating summarization strategies and developing tools and resources for that purpose. The design of the discourse annotation tasks and the decisions that have been taken during the annotation process are detailed in this paper.
---
paper_title: Terminology extraction: an analysis of linguistic and statistical approaches

paper_content:

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
---
paper_title: Assessing Agreement On Classification Tasks: The Kappa Statistic

paper_content:

Currently, computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several different statistics, none of which are easily interpretable or comparable to each other. Meanwhile, researchers in content analysis have already experienced the same difficulties and come up with a solution in the kappa statistic. We discuss what is wrong with reliability measures as they are currently used for discourse and dialogue work in computational linguistics and cognitive science, and argue that we would be better off as a field adopting techniques from content analysis.
---
paper_title: The design, implementation, and use of the Ngram statistics package

paper_content:

The Ngram Statistics Package (NSP) is a flexible and easy-to-use software tool that supports the identification and analysis of Ngrams, sequences of N tokens in online text. We have designed and implemented NSP to be easy to customize to particular problems and yet remain general enough to serve a broad range of needs. This paper provides an introduction to NSP while raising some general issues in Ngram analysis, and summarizes several applications where NSP has been successfully employed. NSP is written in Perl and is freely available under the GNU Public License.
---
paper_title: EχATOLP – An Automatic Tool for Term Extraction from Portuguese Language Corpora

paper_content:

This paper describes EχATOLP, a software tool to extract significant terms from an annotated corpus written in portuguese about a specific domain of interest. Being based on linguistic and statistical approaches, this tool extracts terms that are frequent and syntactic relevant to the domain of interest.
---
paper_title: The C-value/NC-value Method of Automatic Recognition for Multi-Word Terms

paper_content:

Technical terms (henceforth called simply terms), are important elements for digital libraries. In this paper we present a domain-independent method for the automatic extraction of multi-word terms, from machine-readable special language corpora. ::: ::: The method, (C-value/NC-value), combines linguistic and statistical information. The first part, C-value enhances the common statistical measure of frequency of occurrence for term extraction, making it sensitive to a particular type of multi-word terms, the nested terms. The second part, NC-value, gives: 1) a method for the extraction of term context words (words that tend to appear with terms), 2) the incorporation of information from term context words to the extraction of terms.
---
paper_title: Extracting compound terms from domain corpora

paper_content:

The need for domain ontologies motivates the research on structured information extraction from texts. A foundational part of this process is the identification of domain relevant compound terms. This paper presents an evaluation of compound terms extraction from a corpus of the domain of Pediatrics. Bigrams and trigrams were automatically extracted from a corpus composed by 283 texts from a Portuguese journal, Jornal de Pediatria, using three different extraction methods. Considering that these methods generate an elevated number of candidates, we analyzed the quality of the resulting terms according to different methods and cut-off points. The evaluation is reported by metrics such as precision, recall and f-measure, which are computed on the basis of a hand-made reference list of domain relevant compounds.
---
paper_title: Applying transductive learning for automatic term extraction: The case of the ecology domain

paper_content:

Terms are the basis for general text mining and natural language processing applications. However, the manual term extraction is unfeasible due to the huge number of words presented in a domain corpus and also the human effort required to do the extraction. For the term extraction task, machine learning techniques have been used to perform automatic term extraction (ATE). Inductive learning is commonly used, but it requires a large number of words labeled as terms and non-terms to build a classification model and classify unseen words. A better solution is the use of transductive learning, since it requires a small number of labeled examples to classify words as terms and non-terms. In this paper, we propose the use of transductive learning to ATE. The obtained results demonstrate that the application of transductive learning to ATE produces better results than the results obtained by inductive learning.
---
paper_title: A comparative study on unsupervised feature selection methods for text clustering

paper_content:

Text clustering is one of the central problems in text mining and information retrieval area. For the high dimensionality of feature space and the inherent data sparsity, performance of clustering algorithms will dramatically decline. Two techniques are used to deal with this problem: feature extraction and feature selection. Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, four unsupervised feature selection methods, DF, TC, TVQ, and a new proposed method TV are introduced. Experiments are taken to show that feature selection methods can improves efficiency as well as accuracy of text clustering. Three clustering validity criterions are studied and used to evaluate clustering results.
---
paper_title: A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set

paper_content:

In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
---
paper_title: Improving portuguese term extraction

paper_content:

This paper presents the evaluation of a set of heuristics to improve the quality of extracted terms from an annotated domain corpus written in Portuguese. The proposed heuristics start from part-of-speech and grammatical functional annotation of texts, identifying nouns and noun phrases that are the best candidates to be considered terms of the domain. These nouns and noun phrases are submitted to a set of approximative rules (heuristics) that may either discard some, accept others (removing words or not), or even discover implicit terms that can be inferred. The effectiveness of these heuristics is verified through a corpus experiment, on the basis of a reference list for which usual metrics are computed.
---
paper_title: The design, implementation, and use of the Ngram statistics package

paper_content:

The Ngram Statistics Package (NSP) is a flexible and easy-to-use software tool that supports the identification and analysis of Ngrams, sequences of N tokens in online text. We have designed and implemented NSP to be easy to customize to particular problems and yet remain general enough to serve a broad range of needs. This paper provides an introduction to NSP while raising some general issues in Ngram analysis, and summarizes several applications where NSP has been successfully employed. NSP is written in Perl and is freely available under the GNU Public License.
---
paper_title: The TermiNet Project: an Overview

paper_content:

Linguistic resources with domain-specific coverage are crucial for the development of concrete Natural Language Processing (NLP) systems. In this paper we give a global introduction to the ongoing (since 2009) TermiNet project, whose aims are to instantiate a generic NLP methodology for the development of terminological wordnets and to apply the instantiated methodology for building a terminological wordnet in Brazilian Portuguese.
---
paper_title: Managing Gigabytes : Compressing and Indexing documents and images

paper_content:

This book published in 1999 is a revised second edition of the version that first appeared in 1994. The authors have incorporated many changes in the second edition by updating various chapters to include the latest developments. The book is concerned with the task of managing large volumes of text and image data amounting to several gigabytes. The fundamental problems addressed in the book include compressing such data and indexing it, in order to enable easy search. Many books look at compressing and indexing as if they are unrelated techniques; however, this book brings out the advantages of combining them in a beneficial way.
---
paper_title: An Evaluation on Feature Selection for Text Clustering

paper_content:

Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called "Term Contribution (TC)" and perform a comparative study on a variety of feature selection methods for text clustering, including Document Frequency (DF), Term Strength (TS), Entropy-based (En), Information Gain (IG) and χ2 statistic (CHI). Finally, we propose an "Iterative Feature Selection (IF)" method that addresses the unavailability of label problem by utilizing effective supervised feature selection method to iteratively select features and perform clustering. Detailed experimental results on Web Directory data are provided in the paper.
---
paper_title: A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set

paper_content:

In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
---
paper_title: A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set

paper_content:

In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
---
paper_title: The TermiNet Project: an Overview

paper_content:

Linguistic resources with domain-specific coverage are crucial for the development of concrete Natural Language Processing (NLP) systems. In this paper we give a global introduction to the ongoing (since 2009) TermiNet project, whose aims are to instantiate a generic NLP methodology for the development of terminological wordnets and to apply the instantiated methodology for building a terminological wordnet in Brazilian Portuguese.
---

Title: A survey of automatic term extraction for Brazilian Portuguese

Section 1: Background
Description 1: Provide an overview of the importance and challenges of automatic term extraction (ATE) for Brazilian Portuguese, outlining historical context and the significance of terminological units in specialized texts.

Section 2: Problems of the automatic term extraction
Description 2: Discuss various issues and complexities involved in ATE, including the identification and validation of candidate terms, subjective nature of term recognition, and specific terminological features.

Section 3: Approaches for the extraction of candidate terms
Description 3: Detail the primary methods used for ATE, including statistical, linguistic, and hybrid approaches, explaining their processes and providing examples of each.

Section 4: The statistical approach
Description 4: Elaborate on the purely statistical methods used in ATE, discussing specific measures like frequency, mutual information, and document frequency, as well as their advantages and limitations.

Section 5: The linguistic approach
Description 5: Focus on methods that use linguistic knowledge for ATE, such as morphological, syntactic, and semantic analysis, and outline the processes involved in identifying candidate terms through linguistic properties.

Section 6: The hybrid approach
Description 6: Explore techniques that combine both statistical and linguistic methods for improved accuracy in ATE, citing specific hybrid measures and their applications.

Section 7: Evaluation measures for term extraction
Description 7: Describe the different metrics and criteria used to evaluate the performance of ATE systems, including precision, recall, F-measure, and context term weight (ctw), as well as the role of gold standards.

Section 8: Corpora for Portuguese
Description 8: Provide a description of the corpora available for ATE in Brazilian Portuguese, detailing specific datasets, their characteristics, and their uses in term extraction research.

Section 9: State of the art of term extraction in Brazilian Portuguese
Description 9: Review the most significant contributions and research developments in ATE for Brazilian Portuguese, highlighting methods and techniques from various studies.

Section 10: Research developed for Brazilian Portuguese term extraction
Description 10: Summarize specific research projects and studies focused on ATE in Brazilian Portuguese, detailing their methodologies, findings, and impact.

Section 11: Research related to Brazilian Portuguese term extraction
Description 11: Present discussions and findings related to the broader context of ATE in Brazilian Portuguese, including analyses of manual vs. automatic extraction methods and specific domain studies.

Section 12: Projects related to the Brazilian Portuguese term extraction
Description 12: Outline major projects and initiatives that involve ATE for Brazilian Portuguese, explaining their goals, methodologies, and contributions to the field.

Section 13: Discussion about the state of the art in term extraction for the Brazilian Portuguese language
Description 13: Provide a critical discussion of the current state of ATE for Brazilian Portuguese, including strengths, gaps, and future directions in research.

Section 14: Conclusions
Description 14: Summarize the main findings of the survey, reflecting on the progress made in ATE for Brazilian Portuguese and suggesting implications for future research.