1999 LearningDictsForIEbyBootstrapping

Subject Headings: Semi-Supervised Named Entity Recognition Algorithm.

Notes

Presentation at http://www.cs.cmu.edu/~wcohen/10-707/ppts/Yi-Chia.ppt
Presents an unsupervised learning algorithm for named entity (and nominal) detection & classification
Uses a wordlist for each category that contains Words and Phrases that belong to a given category.
Uses a list of Lexical Patterns to specify contexts typically associated with a given category. E.g. “operates in x."
AutoSlog is used to generate the patterns.
Commences with a "seed list" of words that are known to be in-category.
The algorithm then uses bootstrapping to improve their pattern identification.
- Finds the pattern that best matches the current wordlist.
- "Best match" is determined by a combination of precision and coverage.
- Update the pattern list.
- Update the worlist of the words and phrases extracted by the new pattern.

(Agichtein & Gravano, 2000) ⇒ Eugene Agichtein, and Luis Gravano. (2000). “Snowball: Extracting Relations from Large Plain-Text Collections.” In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL 2000).

Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multi-level bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories.

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Learning to Extract Symbolic Knowledge from the World Wide W.. - Craven, DiPasquo et al. - 1998
Learning Information Extraction Rules for Semi-structured an.. - Soderland - 1999
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Automatically Constructing a Dictionary for Information Extr.. - Riloff - 1993
Automatically Generating Extraction Patterns from Untagged T.. - Riloff
Learning information extraction patterns from examples - Huffman - 1996
An Empirical Study of Automated Dictionary Construction for .. - Riloff
Relational Learning Techniques for Natural Language Informat.. - Califf - 1998
Toward General-Purpose Learning for Information Extraction - Freitag - 1998
Noun-phrase Cooccurrence Statistics for Semi-automatic Seman.. - Roark, Charniak - 1998
A Corpus-based Approach for Building Semantic Lexicons - Riloff, Shepherd - 1997
Acquisition of Semantic Patterns for Information Extraction .. (context) - Berlin, Kim et al. - 1993
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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 LearningDictsForIEbyBootstrapping	Ellen Riloff Rosie Jones			Learning Dictionaries for Information Extraction by Multi-level Bootstrapping		Proceedings of AAAI Conference	http://www.cs.utah.edu/~riloff/pdfs/aaai99.pdf			1999