Named Entity Recognition - ACL 2011 Presentation
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Named Entity Recognition - ACL 2011 Presentation
- 1. The Web is not a PERSON, Berners- Lee is not an ORGANIZATION, and African-Americans are not LOCATIONS: An Analysis of the Performance of Named-Entity Recognition Robert Krovetz (Lexicalresearch.com), Paul Deane, Nitin Madnani (ETS) A Review by Richard Littauer (UdS)
- 2. The Background Named-Entity Recognition (NER) is normally judged in the context of Information Extraction (IE)
- 3. The Background Named-Entity Recognition (NER) is normally judged in the context of Information Extraction (IE) Various competitions
- 4. The Background Named-Entity Recognition (NER) is normally judged in the context of Information Extraction (IE) Various competitions Recently: ◦ non-English languages ◦ improving unsupervised learning methods
- 5. The Background “There are no well-established standards for evaluation of NER.”
- 6. The Background “There are no well-established standards for evaluation of NER.” ◦ Criteria for NER system changes for competitions ◦ Proprietary software
- 7. The Background KDM wanted to identify MWEs…
- 8. The Background KDM wanted to identify MWEs… … but false positives, tagging inconsistencies stopped this.
- 9. The Background KDM wanted to identify MWEs… … but false positives, tagging inconsistencies stopped this. IE derives Recall and Precision from Information Retrieval NER is just a small part of this, so is rarely evaluated independently
- 10. The Background So, they want to test NER systems, and provide a unit test based on the problems encountered
- 11. Evaluation Compared three NER taggers: Stanford: ◦ CRF, 100m training corpus; University of Illinois (LBJ): ◦ Regularized average perceptron, Reuters 1996 News Corpus; BBN IdentiFinder (IdentiFinder): ◦ HMMs, commercial
- 12. Evaluation Agreement on Classification
- 13. Evaluation Agreement on Classification Ambiguity in Discourse
- 14. Evaluation Agreement on Classification Ambiguity in Discourse Stanford vs. LBJ on internal ETS 425m corpus All three on American National Corpus
- 15. Stanford vs. LBJ NER reported as 85-95% accurate.
- 16. Stanford vs. LBJ NER reported as 85-95% accurate. Same number for both: 1.95m for Stanford, 1.8m for LBJ (7.6% difference) However, errors:
- 17. Stanford vs. LBJ Agreement:
- 18. Stanford vs. LBJ Ambiguity:
- 19. Stanford vs. LBJ vs. IdentiFinder Agreement:
- 20. Stanford vs. LBJ vs. IdentiFinder Agreement:
- 21. Stanford vs. LBJ vs. IdentiFinder Differences: ◦ How they are tokenized ◦ Number of entities recognized overall
- 22. Stanford vs. LBJ vs. IdentiFinder Ambiguity:
- 23. Unit Test Created two documents that can be used as texts ◦ Different cases for true positives of PERSON, LOCATION, ORGANIZATION ◦ Entirely upper case not NE (Ex. AAARGH) ◦ Punctuated terms not NE ◦ Terms with Initials ◦ Acronyms (some expanded, some not) ◦ Last names in close proximity to first names
- 24. Unit Test Created two documents that can be used as texts ◦ Terms with prepositions (Mass. Inst. Of Tech.) ◦ Terms with location and organization (Amherst College) Provided freely online.
- 25. One NE Tag per Discourse Unusual for multiple occurrences of a token in a document to be different entities True for homonyms An exception: Location + sports team
- 26. One NE Tag per Discourse Stanford, LBJ have features for non- local dependencies to help with this. KDM: Two other uses for NLD: ◦ Source of error in evaluation ◦ A way to identify semantically related entities These should be treated as exceptions
- 27. Discussion There are guidelines for NER – but we need standards. The community should focus on PERSON, ORGANISATION, LOCATION, and MISC. ◦ Harder to deal with than Dates, Times. ◦ Disagreement between taggers. ◦ MISC is necessary. ◦ These have important value elsewhere.
- 28. Discussion To improve intrinsic evaluation for NER: 1. Create test sets for divers domains. 2. Use standardized sets for different phenomena. 3. Report accuracy for POL separately. 4. Establish uncertainty in the tagging system.
- 29. Conclusion 90% accuracy not real. We need to use only entities that are agreed on by multiple taggers. Even in cases where they both disagree (Hint: Future work.) Unit test downloadable.
- 30. Cheers/PERSON Richard/ORGANISATION thanks the Mword Class/LOCATION for listening to his talk about Berners-Lee/MISC
Notes de l'éditeur
- NER: The Aim is to recognize and classify different types of entities (names, organizations, locations, dates, etc.)
- Not sure why they focused on competitions, to be honest. But they mention the Message Understanding Conference, and CoNLL.
- They give two possible reasons for this:
- Part of the problem is that
- No Gold Standards for any of these. So, they compared on two levels
- How well do they work on PERSON, ORGANIZATION, and LOCATION? How much to they agree? What mistakes?
- How frequently does each tagger produce multiple classifications for the same entity in a single document? Clinton as a person, and place, for instance.
- ANC tagged for IdentiFinder already.
- However, this was often not consistent
- Identifiner got much more ORGANISATION than the others. Also uses extra class, Geo-Political Entity
- Existing taggers treat the non-local dependencies as a way of dealing with the sparse data problem, and as a way to resolve tagging differences by look- ing at how often one token is classified as one type versus another.
- 1. They didn’t do this. 2. And actually use them, not just one of them. 3. Report accuracy rates separately for the three major classes. Accuracy rates should be further broken down according to the items in the unit test that are designed to assess mistakes: or- thography, acronym processing, frequent false positives, and knowledge-based classification.They go on to say that ANC is doing it right, but is too small, hence their ETS corpus.
- 1. They didn’t do this. 2. And actually use them, not just one of them. 3. Report accuracy rates separately for the three major classes. Accuracy rates should be further broken down according to the items in the unit test that are designed to assess mistakes: or- thography, acronym processing, frequent false positives, and knowledge-based classification.They go on to say that ANC is doing it right, but is too small, hence their ETS corpus.
- 1. They didn’t do this. 2. And actually use them, not just one of them. 3. Report accuracy rates separately for the three major classes. Accuracy rates should be further broken down according to the items in the unit test that are designed to assess mistakes: or- thography, acronym processing, frequent false positives, and knowledge-based classification.They go on to say that ANC is doing it right, but is too small, hence their ETS corpus.