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Optimizing Dense Retrieval Model Training with Hard Negatives ...
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Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently ...
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THUIR at TREC2008: Blog Track
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In: DTIC (2008)
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Abstract:
This is the second year that the IR groups of Tsinghua University participated in TREC Blog Track. Different from the previous track, TREC introduced a new task, the polarity finding task. So, we focus on 3 main tasks this year. The opinion retrieval task involves locating blog posts that express an opinion about a given target. The target can be a "traditional" named entity -- a name of a person, location, or organization -- but also a concept (such as a type of technology), a product name, or an event. The topic of the post does not necessarily have to be the target, but an opinion about the target must be present in the post or one of the comments to the post. The polarity task is to locate blog posts that express an idea either positive or negative about a target. For relevant task, a multi-field relevance ranking based on probabilistic retrieval model has been used. Both feed content and permalink content are used. Two kinds of information fusion have been experimented. One is the result combination on both parts. Another is to combine the two corpus in the weighting phase with improved algorithms. Experimental results on training set showed that both methods are proved to be effective and the second way seemed to be more stable. For opinion finding tasks, the combination of relevance score and opinionate score use a unified generation model is emphasized. The final score of one document is a quadratic combination of sentiment score given by an opinion generation model and the relevance score given by document generation model. HowNet has been used as the sentimental lexicon. For polarity task, several algorithms on using sentiment words co-occurrence frequency are implemented. The selection of the sentiment dictionaries and the effectiveness of co-occurrence window size are studied. The approach of using polarity words as query terms on first-step relevance results is also performed. ; Presented at the Text Retrieval Conference (17th), held in Gaithersburg, Maryland, on 18-21 November 2008. Sponsored in part by Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA).
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Keyword:
*DATA FUSION; *INFORMATION RETRIEVAL; *TREC(TEXT RETRIEVAL EXPERIMENTATION); ALGORITHMS; CHINA; DICTIONARIES; FOREIGN REPORTS; Information Science; IR(INFORMATION RETRIEVAL); MODELS; POLARITY; PROBABILITY; PUBLIC OPINION; QUERIES; SYMPOSIA; TARGETS; TRAINING
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URL: http://www.dtic.mil/docs/citations/ADA512724
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA512724
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