NLPComp in TAC 2012 Entity Linking and Slot-Filling
نویسندگان
چکیده
The NLPComp team participated in two TACKBP2012 tasks: Regular Entity Linking and Regular Slot Filling. For the entity linking task, a three-step entity linking system is developed. In the first step, a list of possible candidates are selected. Then the best candidate is identified to decide whether a link exists. In addition, a document clustering algorithm is used to group NIL queries. This system uses the Wikipedia anchor terms to enlarge the number of candidate instances. It then incorporates the topic modelling technique to select features of topical words. However, our system produces a poor answer coverage and the NIL detection system brings significant loss in the final Fscore. For the slot filling task, we developed a system which combines rule-based approach and multiple instance learning technique. In rulebased slot filling, a number of trigger words are collected from the English Wikipedia and the frequency feature is used to select final slot value(s). When extracting slot value(s) using the multiple instance learning technique, 2009 and 2010 KBP slot filling queries and manually annotated answers are used to create named entity pairs which exhibit a particular relation. Then bags of sentences containing named entity pair are extracted from the KBP source. Our system reaches the median level among all the participating systems.
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