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Exploiting Named Entity Taggers in a Second Language

Exploiting Named Entity Taggers in a Second Language. Thamar Solorio Computer Science Department National Institute of Astrophysics, Optics and Electronics ACL 2005 Student Research Workshop. Abstract. Named Entity Recognition (X) Complex linguistic resources (X) A hand coded system

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Exploiting Named Entity Taggers in a Second Language

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  1. Exploiting Named Entity Taggers in a Second Language Thamar Solorio Computer Science Department National Institute of Astrophysics, Optics and Electronics ACL 2005 Student ResearchWorkshop

  2. Abstract • Named Entity Recognition • (X) Complex linguistic resources • (X) A hand coded system • (X) Any language dependent tools • The only information we use is automatically extracted from the documents, without human intervention. • Our approach even outperformed the hand coded system on NER in Spanish, and it achieved high accuracies in Portuguese.

  3. Introduction • Most NER approaches have very low portability • Many NE extractor systems rely heavily on complex linguistic resources, which are typically hand coded, for example regular expressions, grammars, gazetteers and the like. • Adapting a system to a different collection or language requires a lot of human effort: rewriting the grammars, acquiring new dictionaries, searching trigger words, and so on. • (O) NE extractor system for Spanish + Portuguese corpus • (X) developing linguistic tools such as parsers, POS taggers, grammars and the like.

  4. Related Work (1/2) • Hidden Markov Models • Zhou and Su, 2002 • Internal features: gazetteer information • External features: the context of other NEs already recognized • Bikel et al., 1997 and Bikel et al., 1999 • Maximum entropy • Borthwick, 1999: dictionaries and other orthographic information • Carreras et al.,2003 • presented results of a NER system for Catalan using Spanish resources • using cross-linguistic features

  5. Related Work (2/2) • Petasis et al., 2000 • extending a proper noun dictionary • an inductive decision-tree classifier • unsupervised probabilistic learning of syntactic and semantic context • POS tags and morphological information • Arévalo et al., 2002 • External information provided by gazetteers and lists of trigger words • A context free grammar, manually coded, is used for recognizing syntactic patterns

  6. Data sets • The corpus in Spanish is that used in the CoNLL 2002 competitions for the NE extraction task. • training: 20,308 NEs • testa: 4,634 NEs • to tune the parameters of the classifiers (development set) • performed experiments with testa only • testb: 3,948 NEs • to compare the results of the competitors • The corpus in Portuguese is “HAREM: Evaluation contest on named entity recognition for Portuguese”. This corpus contains newspaper articles and consists of 8,551 words with 648 NEs.

  7. Two-step Named Entity Recognition • Named Entity Delimitation (NED) • Determining boundaries of named entities • Named Entity Classification (NEC) • Classifying the named entities into categories

  8. Named Entity Delimitation (1/3) • BIO scheme • We used a modified version of C4.5 algorithm (Quinlan, 1993) implemented within the WEKA environment (Witten and Frank, 1999). • For each word we combined two types of features: • Internal: word itself, orthographic information(1~6 possible states) and the position in the sentence. • External: POS tag and BIO tag • A given word w are extracted using a window of five words anchored in the word w, each word described by the internal and external features mentioned previously. • Our classifier learns to discriminate among the three classes and assigns labels to all the words, processing them sequentially.

  9. Named Entity Delimitation (2/3)

  10. Named Entity Delimitation (3/3) • The hand coded system used in this work was developed by the TALP research center (Carreras and Padró, 2002). • NLP analyzers for Spanish, English and Catalan • Include practical tools such as POS taggers, semantic analyzers and NE extractors. • This NER system is based on hand-coded grammars, lists of trigger words and gazetteer information.

  11. NED - Experimental Results • Average of several runs of 10-fold cross-validation

  12. Named Entity Classification • To build a training set for the NEC learner: • the same attributes as for the NED task • suffix of each word. maximum size of 5 characters. • Spanish: person, organization, location and miscellaneous. • Portuguese: person, object, quantity, event, organization, artifact, location, date, abstraction and miscellaneous. • We believe that the learner will be capable of achieving good accuracies in the learning task.

  13. NEC - Experimental Results (1/2) • Similarly to the NED case we trained C4.5 classifiers for the NEC task

  14. NEC - Experimental Results (2/2) Only 4 instances

  15. Conclusions • NER systems must be easy to port and robust, given the great variety of documents and languages for which it is desirable to have these tools available. • Our method does not require any language dependent features. The only information used in this approach is automatically extracted from the documents, without human intervention. • Our method has shown to be robust and easy to port to other languages. The only requirement for using our method is a tokenizer for languages.

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