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2	System Architecture
3	What’s New This Year Approximate matching of grammatical dependency relations for answer extraction Soft matching patterns in identifying definition sentences. See [Cui et al., 2004a] and [Cui et al., 2004b] Exploiting definitions to answer factoid and list questions.
4	Outline System architecture New Features in TREC-13 QA Main Task Approximate Dependency Relation Matching for Answer Extraction Soft Matching Patterns for Definition Generation Definition Sentences in Answering Topically-Related Factoid/List Questions Conclusion
5	Dependency Relation Matching in QA Tried before PIQASso and MIT systems have applied dependency relations in QA. Used exact match of relations to extract answers directly. Why need to consider dependency relations? An upper bound of 70% for answer extraction (Light et al., 2001) Many NE’s with the same type appearing close to each other. Some questions don’t have NE-type targets. E.g. what does AARP stand for?
6	Extracting Dependency Relation Triples Minipar-based (Lin, 1998) dependency parsing Relation triple: two anchor words and their relationship E.g. <“desk”, complement, “on”> for “on the desk”. Relation path: path of relations between two words E.g., <“desk”, mod, complement “floor”> for “on the desk at the fourth floor”
7	Examples of relation triples Q: What American revolutionary general turned over West Point to the British? q1) General sub obj West Point q2) West Point mod pcomp-n British A: …… Benedict Arnold’s plot to surrender West Point to the British …… s1) Benedict Arnold poss s sobj West Point s2) West Point mod pcomp-n British Can’t be extracted by exact match of relations.
8	Learning Relation Similarity We need a measure to find the similarity between two different paths. Adopt a statistical method to learn similarity from past QA pairs. Training data preparation Around 1,000 factoid question-answer pairs from the past two years’ TREC QA task. Extract all relation paths between all non-trivial words 2,557 path pairs. Align the paths according to identical anchor nodes.
9	Using Mutual Information to Measure Relation Co-occurrence Two relations’ similarity measured by their co-occurrences in the question and answer paths. Variation of mutual information (MI) a to discount the score of two relations appearing in long paths.
10	Measuring Path Similarity – 1 We adopt two methods to compute path similarity using different relation alignment methods. Option 1: ignore the words of those relations along the given paths – Total Path Matching. A path consists of only a list of relations. Relation alignment by permutation of all possibilities. Adopt IBM’s Model 1 for statistical translation:
11	Measuring Path Similarity – 2 Option 2: consider the words of those relations along a path – Triple Matching. A path consists of a list of relations and their words. Only those relations with matched words count. Deliberately ignore long dependency relationship.
12	Selecting Answer Strings Statistically Use the top 50 ranked sentences from the passage retrieval module for answer extraction. Evaluate the path similarity for relation paths between the question target / answer candidate and other question terms. Non-NE questions: evaluate all noun/verb phrases.
13	Discussions on Evaluation Results The use of approximate relation matching outperforms our previous answer extraction technique. 22% improvement for overall questions. 45% improvement for Non-NE questions (69 out of 230 questions). The two path similarity measurements do not make obvious difference. Total Path Matching performs slightly better than Triple Matching. Triple Matching doesn’t degrade the performance because Minipar can’t resolve long distance dependency as well.
14	Outline System architecture New Experiments in TREC-13 QA Main Task Approximate Dependency Relation Matching for Answer Extraction Soft Matching Patterns for Definition Generation Definition Sentences in Answering Topically-Related Factoid/List Questions Conclusion
15	Question Typing and Passage Retrieval for Factoid/List Q’s Question typing Leveraging our past question typology and rule-based question typing module. Offline tagging of the whole TREC corpus using our rule-based named entity tagger. Passage retrieval – on two sources: Topic-relevant document set by the document retrieval module: NUSCHUA1 and 2. Definition sentences for a specific topic by the definition generation module: NUSCHUA3 Question-specific wrappers on definitions.
16	Exploiting Definition Sentences to Answer Factoid/List Questions Conduct passage retrieval for factoid/list questions on the definition sentences about the topic. Much more efficient due to smaller search space. Average accuracy of 0.50, lower than that over all topic-related documents. Due to low recall – imposed cut-off for selecting definition sentences (naïve use of definitions). Some sentences for answering factoid/list questions are not definition sentences.
17	Exploiting Definitions from External Knowledge Pre-complied wrappers for extraction of specific fields of information for list questions Works, product names and person titles. From both generated definition sentences and existing definitions: cross validation. Achieves F-measure of 0.81 for 8 list questions about works.
18	Outline System architecture New Experiments in TREC-13 QA Main Task Approximate Dependency Relation Matching for Answer Extraction Soft Matching Patterns for Definition Generation Definition Sentences in Answering Topically-Related Factoid/List Questions Conclusion
19	Conclusion Approximate relation matching for answer extraction Still have a hard time in dealing with difficult questions. Dependency relation alignment problem – words often can’t be matched due to linguistic variations. Semantic matching of words/phrases is needed with relation matching. More effective use of topic related sentences in answering factoid/list questions.
20	Q & A