Cover image for Named Entities : Recognition, classification and use.
Named Entities : Recognition, classification and use.
Title:
Named Entities : Recognition, classification and use.
Author:
Sekine, Satoshi.
ISBN:
9789027289223
Personal Author:
Physical Description:
1 online resource (176 pages)
Contents:
Named Entities -- Editorial page -- Title page -- LCC data -- Table of contents -- Foreword -- A survey of named entity recognition and classification -- Introduction -- 1. Observations: 1991 to 2006 -- 1.1 Language factor -- 1.2 Textual genre or domain factor -- 1.3 Entity type factor -- 1.4 What's next? -- 2. Learning methods -- 2.1 Supervised learning -- 2.2 Semi-supervised learning -- 2.3 Unsupervised learning -- 3. Feature space for NERC -- 3.1 Word-level features -- 3.2 List lookup features -- 3.3 Document and corpus features -- 4. Evaluation of NERC -- 4.1 MUC evaluations -- 4.2 Exact-match evaluations -- 4.3 ACE evaluation -- 5. Conclusion -- Acknowledgement -- Notes -- References -- Summary -- Diversity in logarithmic opinion pools -- Introduction -- 1. Logarithmic Opinion Pools -- 1.1 Definition -- 1.2 The Ambiguity Decomposition -- 1.3 LOPs for CRFs -- 2. Sources of diversity -- 2.1 Offline Diversity -- 2.2 Online Diversity -- 3. Experiments -- 3.1 Dataset -- 3.2 Offline diversity -- 3.3 Online Diversity -- Conclusion -- Note -- References -- Summary -- Handling conjunctions in named entities -- Introduction -- 1. Problem Description -- 2. Previous Work -- 2.1 Rau -- 2.2 Coates-Stephens -- 2.3 McDonald -- 2.4 Mikheev et al. -- 2.5 Downey et al -- 3. Our Approach -- 4. Experimental setup -- 4.1 Corpus and Data Preparation -- 4.2 The Tag Set -- 4.3 Encoding -- 4.4 Baseline and Algorithms -- 5. Results -- 5.1 Evaluation Scheme -- 5.2 Classification Results -- 6. Analysis -- 6.1 Conjunction Category Indicators -- 6.2 Error Analysis -- 7. Conclusions -- Notes -- References -- Summary -- Complex named entities in Spanish texts -- Introduction -- 1. Corpus characteristics -- 2. Named entities formed by a word or series of words initialized with a capital letter -- 3. Named entities including prepositions.

3.1 Named entities including preposition "de" -- 3.2 Named entities including preposition "a" -- 3.3 Named entities including preposition "contra" -- 3.4 Named entities including preposition "sobre" -- 4.1 Syntactic ambiguity -- 4.2 Embedded named entities -- 4. Named entities formed with conjunction -- 5. Discourse structures -- 6. Discussion -- Conclusion -- Note -- References -- Summary -- Named Entity Recognition and transliteration in Bengali -- 0. Introduction -- 0.1 Named Entity Recognition -- 0.2 Named Entity Transliteration -- 1. Named Entity Recognition in Bengali -- 1.1 Models of Named Entity Recognition -- 2. Named Entity Transliteration -- 2.1 Transliteration Models -- 2.2 Bengali to English Machine Transliteration -- 3. Experimental Results of the NER System -- 3.1 Evaluation Method of the NER System -- 3.2 Results of the NER System -- 4. Experimental Results of the Transliteration System -- 4.1 Evaluation Method of the Transliteration System -- 4.2 Results of the Transliteration Models -- Conclusion -- Note -- References -- Summary -- A note on the semantic and morphological properties of proper names in the Prolex project -- Introduction -- 2. The morphology of proper names -- 2.1 Inflection -- 2.2 Regular derivation -- 2.3 Surnames -- 2.4 The morphology of multi-word proper names -- 3. Lexical resources for proper names -- 3.1 E-dictionary of proper names and text analysis -- 3.2 The ontology of proper names -- 4. Processing with the Prolex multilingual ontology -- 4.1 The morphological expansion of entities - the example of the prolexeme Naples -- 4.2 Semantical expansion of entities - the example: bivša Jugoslavija -- 5. Conclusion -- Notes -- References -- Summary -- Cross-lingual Named Entity Recognition -- 1. Introduction -- 2. The role of NERC in NewsExplorer -- 3. Algorithms for Named Entity Recognition used in NewsExplorer.

3.1 Named Entity Recognition for person and organisation names -- 3.2 Extraction of geographical location names -- 3.3 Dealing with inflection -- 4. Name variant matching and merging -- 4.1 Related work -- 4.2 Combining variant normalisation and fuzzy matching for name variant detection -- 5. Social network detection for names extracted from the news -- 5.1 Calculation of the social network -- 6. Ongoing and future work -- 7. Discussion and Conclusion -- Acknowledgements -- Notes -- References -- Summary -- Index -- The series Benjamins Current Topics.
Abstract:
Named Entities provides critical information for many NLP applications. Named Entity recognition and classification (NERC) in text is recognized as one of the important sub-tasks of Information Extraction (IE). The seven papers in this volume cover various interesting and informative aspects of NERC research. Nadeau & Sekine provide an extensive survey of past NERC technologies, which should be a very useful resource for new researchers in this field. Smith & Osborne describe a machine learning model which tries to solve the over-fitting problem. Mazur & Dale tackle a common problem of NE and conjunction; as conjunctions are often a part of NEs or appear close to NEs, this is an important practical problem. A further three papers describe analyses and implementations of NERC for different languages: Spanish (Galicia-Haro & Gelbukh), Bengali (Ekbal, Naskar & Bandyopadhyay), and Serbian (Vitas, Krstev & Maurel). Finally, Steinberger & Pouliquen report on a real WEB application where multilingual NERC technology is used to identify occurrences of people, locations and organizations in newspapers in different languages.The contributions to this volume were previously published in Lingvisticae Investigationes 30:1 (2007).
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Added Author:
Electronic Access:
Click to View
Holds: Copies: