1 / 8

Question AnswerinG

Question AnswerinG. Deliverable 3 – Question Processing John Gilmer Michael Foster Adam Ledyard. Overview. Question Classification Question Reformulation Query Expansion Results Related Reading. Question Classification. Used Roth and Li’s Question Classification Taxonomy

eunice
Download Presentation

Question AnswerinG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Question AnswerinG Deliverable 3 – Question Processing John Gilmer Michael Foster Adam Ledyard

  2. Overview • Question Classification • Question Reformulation • Query Expansion • Results • Related Reading

  3. Question Classification • Used Roth and Li’s Question Classification Taxonomy • Trained a MaxEnt Classifier on the 5500 training set • Features used: • Unigrams • POS tag unigrams • NP chunks found by nltk trained chunker using BIO tagged CoNLL 2000 corpus (WSJ text) • Semantic feature based on Roth and Li’s semantic lists • Results: 84% accuracy based on TREC 10 labeled test data.

  4. Question Reformulation • Topic insertion: Topic text is always added to a query: • Replace the first pronoun in the sentence with topic. • Leads to duplicate query terms if partial topic text and a pronoun are in a question. • Potential replacement of incorrect pronoun (none observed). • “How long was it used as a defense?” → “How long was Great Wall of China used as a defense?” • If no pronouns are present, identify capitalized terms in the question that are also in the topic. • Look forward and backward to make sure that only missing terms are inserted. • “What company acquired Wynn’s Mirage Resorts in 2000?” → “What company acquired Steven Wynn’sMirage Resorts in 2000?” • Otherwise, if it isn’t present, insert topic at beginning of query. • “How old was the dam?” → “Johnstown floodhow old was the dam”?

  5. Question Reformulation • Exact topic text queries: • When topic text is inserted, create a second query with the topic text in “double quotes” to force an exact phrase search. • Add quotes regardless of position in the query. • ngrams that came from queries containing double quoted text are given a multiplier to their score during answering processing.

  6. Query Expansion • - Use Question Topic for Wikipedia Query • - If Wikipedia returns a page, it is unambiguous • - Extract first sentence of page and parse and chunk with NLTK • - Extract relevant Noun Phrase Chunks and use them to expand query terms • ISSUES: • - Sometimes topics will not return any Wikipedia Page (i.e. “The tourist massacre in Luxor in 1999”) • -Frequently term expansions are not that helpful as is typical of query expansion efforts

  7. Results

  8. Related reading • Fang, Hui. "A re-examination of query expansion using lexical resources." Proceedings of ACL-08: HLT (2008): 139-147. • Li, Xin, and Dan Roth. "Learning question classifiers." Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 2002. • Stoyanchev, Svetlana, Young Chol Song, and William Lahti. "Exact phrases in information retrieval for question answering." Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering. Association for Computational Linguistics, 2008.

More Related