際際滷shows by User: JorgeBaptista4 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: JorgeBaptista4 / Thu, 09 Oct 2014 08:20:25 GMT 際際滷Share feed for 際際滷shows by User: JorgeBaptista4 Rassi et-al propor-2014 /slideshow/rassi-etal-propor2014/40066847 rassi-et-alpropor-2014-141009082025-conversion-gate02
This paper describes a methodology for automatically iden- tifying proverbs and their variants in running texts. This methodology is based on existing compilations of proverbs, by exploring the regular syntactic structures that most proverbs present and intersecting syntac- tic structure with the lexical units of the proverbs. From the syntactic regularities we divided the data into 13 different classes. Finite-state au- tomata is used to represent the regular patterns found in the classes. The results showed a precision rate of 74.68% tested in Brazilian Portuguese journalistic corpus.]]>
This paper describes a methodology for automatically iden- tifying proverbs and their variants in running texts. This methodology is based on existing compilations of proverbs, by exploring the regular syntactic structures that most proverbs present and intersecting syntac- tic structure with the lexical units of the proverbs. From the syntactic regularities we divided the data into 13 different classes. Finite-state au- tomata is used to represent the regular patterns found in the classes. The results showed a precision rate of 74.68% tested in Brazilian Portuguese journalistic corpus.]]> Thu, 09 Oct 2014 08:20:25 GMT /slideshow/rassi-etal-propor2014/40066847 JorgeBaptista4@slideshare.net(JorgeBaptista4) Rassi et-al propor-2014 JorgeBaptista4 This paper describes a methodology for automatically iden- tifying proverbs and their variants in running texts. This methodology is based on existing compilations of proverbs, by exploring the regular syntactic structures that most proverbs present and intersecting syntac- tic structure with the lexical units of the proverbs. From the syntactic regularities we divided the data into 13 different classes. Finite-state au- tomata is used to represent the regular patterns found in the classes. The results showed a precision rate of 74.68% tested in Brazilian Portuguese journalistic corpus. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rassi-et-alpropor-2014-141009082025-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper describes a methodology for automatically iden- tifying proverbs and their variants in running texts. This methodology is based on existing compilations of proverbs, by exploring the regular syntactic structures that most proverbs present and intersecting syntac- tic structure with the lexical units of the proverbs. From the syntactic regularities we divided the data into 13 different classes. Finite-state au- tomata is used to represent the regular patterns found in the classes. The results showed a precision rate of 74.68% tested in Brazilian Portuguese journalistic corpus.
Rassi et-al propor-2014 from Jorge Baptista
]]> 842 3 https://cdn.slidesharecdn.com/ss_thumbnails/rassi-et-alpropor-2014-141009082025-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Body-Part Nouns and Whole-Part Relations in Portuguese /slideshow/markov-etalpropor2014key/40018032 markov-et-al-propor2014-141008081315-conversion-gate02
In this paper, we target the extraction of whole-part rela- tions involving human entities and body-part nouns in SYSTEM, a hy- brid statistical and rule-based Natural Language Processing chain for Portuguese. Whole-part relation is a semantic relation between an entity that is perceived as a constituent part of another entity, or a member of a set.]]>
In this paper, we target the extraction of whole-part rela- tions involving human entities and body-part nouns in SYSTEM, a hy- brid statistical and rule-based Natural Language Processing chain for Portuguese. Whole-part relation is a semantic relation between an entity that is perceived as a constituent part of another entity, or a member of a set.]]> Wed, 08 Oct 2014 08:13:15 GMT /slideshow/markov-etalpropor2014key/40018032 JorgeBaptista4@slideshare.net(JorgeBaptista4) Body-Part Nouns and Whole-Part Relations in Portuguese JorgeBaptista4 In this paper, we target the extraction of whole-part rela- tions involving human entities and body-part nouns in SYSTEM, a hy- brid statistical and rule-based Natural Language Processing chain for Portuguese. Whole-part relation is a semantic relation between an entity that is perceived as a constituent part of another entity, or a member of a set. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/markov-et-al-propor2014-141008081315-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this paper, we target the extraction of whole-part rela- tions involving human entities and body-part nouns in SYSTEM, a hy- brid statistical and rule-based Natural Language Processing chain for Portuguese. Whole-part relation is a semantic relation between an entity that is perceived as a constituent part of another entity, or a member of a set.
Body-Part Nouns and Whole-Part Relations in Portuguese from Jorge Baptista
]]> 314 7 https://cdn.slidesharecdn.com/ss_thumbnails/markov-et-al-propor2014-141008081315-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Corpus annotation for corpus linguistics (nov2009) /slideshow/corpus-annotation-for-corpus-linguistics-nov2009/39503445 corpusannotationforcorpuslinguisticsnov2009-140924225716-phpapp02
Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation; tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation]]>
Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation; tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation]]> Wed, 24 Sep 2014 22:57:16 GMT /slideshow/corpus-annotation-for-corpus-linguistics-nov2009/39503445 JorgeBaptista4@slideshare.net(JorgeBaptista4) Corpus annotation for corpus linguistics (nov2009) JorgeBaptista4 Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation; tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/corpusannotationforcorpuslinguisticsnov2009-140924225716-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Lecture on corpus annotation for corpus linguistics. Contents: DIY corpus, e-texts, character set and text encoding issues, document structure, DTDs, documentation; tools and issues in annotation procedures, good practices; examples from anaphora resolution and named entity recognition annotation campaigns; evaluation of corpus annotation
Corpus annotation for corpus linguistics (nov2009) from Jorge Baptista
]]> 4736 18 https://cdn.slidesharecdn.com/ss_thumbnails/corpusannotationforcorpuslinguisticsnov2009-140924225716-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Corpus linguistics the basics /slideshow/corpus-linguistics-the-basics/39503142 corpuslinguisticsthebasics-140924224447-phpapp02
Introductory lecture on Corpus Linguistics. Contents: Corpus linguistics: past and present, What is a corpus?, Why use computers to study language? Corpus-based vs. Intuition-based approach, Theory vs. Methodology. This lecture was based on McEnery et al. 2006. Corpus-based Language Studies. An Advanced resource book. Routlege.]]>
Introductory lecture on Corpus Linguistics. Contents: Corpus linguistics: past and present, What is a corpus?, Why use computers to study language? Corpus-based vs. Intuition-based approach, Theory vs. Methodology. This lecture was based on McEnery et al. 2006. Corpus-based Language Studies. An Advanced resource book. Routlege.]]> Wed, 24 Sep 2014 22:44:47 GMT /slideshow/corpus-linguistics-the-basics/39503142 JorgeBaptista4@slideshare.net(JorgeBaptista4) Corpus linguistics the basics JorgeBaptista4 Introductory lecture on Corpus Linguistics. Contents: Corpus linguistics: past and present, What is a corpus?, Why use computers to study language? Corpus-based vs. Intuition-based approach, Theory vs. Methodology. This lecture was based on McEnery et al. 2006. Corpus-based Language Studies. An Advanced resource book. Routlege. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/corpuslinguisticsthebasics-140924224447-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Introductory lecture on Corpus Linguistics. Contents: Corpus linguistics: past and present, What is a corpus?, Why use computers to study language? Corpus-based vs. Intuition-based approach, Theory vs. Methodology. This lecture was based on McEnery et al. 2006. Corpus-based Language Studies. An Advanced resource book. Routlege.
Corpus linguistics the basics from Jorge Baptista
]]> 7278 13 https://cdn.slidesharecdn.com/ss_thumbnails/corpuslinguisticsthebasics-140924224447-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Les defis des_langue-pour_le_tal https://fr.slideshare.net/slideshow/les-defis-deslanguepourletal/39150497 lesdefisdeslangue-pourletal-140916095215-phpapp01
Dans cette presentation, nous introduisons des concepts-cl辿s du domaine du traitement automatique de langues (TAL): qu'est-ce qu'un texte pour une machine? comment identifier des unit辿s linguistiques plusieurs niveaux? la segmentation et l'analyse lexicale; la disambiguation automatique; comment relier les mots entre elles? les structures syntaxiques minimales (chunks) et les relations syntaxique d'haute niveau (SUJET, OBJECT DIRECT, etc.); des relations ou r担les s辿mantiques entre les constituants de la phrase; l'unit辿 s辿mantique travers des categories morphosyntaxiques et sa representation dans les lexiques 辿lectroniques.]]>
Dans cette presentation, nous introduisons des concepts-cl辿s du domaine du traitement automatique de langues (TAL): qu'est-ce qu'un texte pour une machine? comment identifier des unit辿s linguistiques plusieurs niveaux? la segmentation et l'analyse lexicale; la disambiguation automatique; comment relier les mots entre elles? les structures syntaxiques minimales (chunks) et les relations syntaxique d'haute niveau (SUJET, OBJECT DIRECT, etc.); des relations ou r担les s辿mantiques entre les constituants de la phrase; l'unit辿 s辿mantique travers des categories morphosyntaxiques et sa representation dans les lexiques 辿lectroniques.]]> Tue, 16 Sep 2014 09:52:15 GMT https://fr.slideshare.net/slideshow/les-defis-deslanguepourletal/39150497 JorgeBaptista4@slideshare.net(JorgeBaptista4) Les defis des_langue-pour_le_tal JorgeBaptista4 Dans cette presentation, nous introduisons des concepts-cl辿s du domaine du traitement automatique de langues (TAL): qu'est-ce qu'un texte pour une machine? comment identifier des unit辿s linguistiques plusieurs niveaux? la segmentation et l'analyse lexicale; la disambiguation automatique; comment relier les mots entre elles? les structures syntaxiques minimales (chunks) et les relations syntaxique d'haute niveau (SUJET, OBJECT DIRECT, etc.); des relations ou r担les s辿mantiques entre les constituants de la phrase; l'unit辿 s辿mantique travers des categories morphosyntaxiques et sa representation dans les lexiques 辿lectroniques. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lesdefisdeslangue-pourletal-140916095215-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Dans cette presentation, nous introduisons des concepts-cl辿s du domaine du traitement automatique de langues (TAL): qu&#39;est-ce qu&#39;un texte pour une machine? comment identifier des unit辿s linguistiques plusieurs niveaux? la segmentation et l&#39;analyse lexicale; la disambiguation automatique; comment relier les mots entre elles? les structures syntaxiques minimales (chunks) et les relations syntaxique d&#39;haute niveau (SUJET, OBJECT DIRECT, etc.); des relations ou r担les s辿mantiques entre les constituants de la phrase; l&#39;unit辿 s辿mantique travers des categories morphosyntaxiques et sa representation dans les lexiques 辿lectroniques.
from Jorge Baptista
]]> 844 35 https://cdn.slidesharecdn.com/ss_thumbnails/lesdefisdeslangue-pourletal-140916095215-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-JorgeBaptista4-48x48.jpg?cb=1523296289 I am Associate Professor at the University of the Algarve and Invited Researcher at Spoken Language Laboratory (L2F), INESC ID Lisboa. I have a Linguistics (Syntax) background and I have been working in Computational Linguistics / Natural Language Processing since the 90's. My interests involve Syntax, Lexical Semantics, Parsing technologies, Word Sense Disambiguation, and Information Retrieval (Named Entities Recognition and Relation Extraction) and Anaphora Resolution. I teach Syntax and NLP-related disciplines. I am also involved in Edition/Text Revision courses. Specialties: Linguistics (Syntax) and Computational Linguistics/Natural Language Processing. https://cdn.slidesharecdn.com/ss_thumbnails/rassi-et-alpropor-2014-141009082025-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/rassi-etal-propor2014/40066847 Rassi et-al propor-2014 https://cdn.slidesharecdn.com/ss_thumbnails/markov-et-al-propor2014-141008081315-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/markov-etalpropor2014key/40018032 Body-Part Nouns and Wh... https://cdn.slidesharecdn.com/ss_thumbnails/corpusannotationforcorpuslinguisticsnov2009-140924225716-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/corpus-annotation-for-corpus-linguistics-nov2009/39503445 Corpus annotation for ...