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| Content Provider | ACM Digital Library |
|---|---|
| Author | Aiello, Luca Maria Lops, Pasquale Molino, Piero |
| Copyright Year | 2016 |
| Description | Author Affiliation: IBM Watson, NY, USA(Universitá degli studi di bari aldo moro, Italy (Lops, Pasquale; Yahoo labs london, UK (Aiello, Luca Maria); Molino, Piero)) |
| Abstract | Community question answering (CQA) sites use a collaborative paradigm to satisfy complex information needs. Although the task of matching questions to their best answers has been tackled for more than a decade, the social question-answering practice is a complex process. The factors influencing the accuracy of question-answer matching are many and hard to disentangle. We approach the task from an application-oriented perspective, probing the space of several dimensions relevant to this problem: features, algorithms, and topics. We gather under a learning to rank framework the most extensive feature set used in literature to date, including 225 features from five different families. We test the power of such features in predicting the best answer to a question on the largest dataset from Yahoo Answers used for this task so far (40M answers) and provide a faceted analysis of the results along different topical areas and question types. We propose a novel family of distributional semantics measures that most of the time can seamlessly replace widely used linguistic similarity features, being more than one order of magnitude faster to compute and providing greater predictive power. The best feature set reaches an improvement between 11% and 26% in P@1 compared to recent well-established state-of-the-art methods. |
| Starting Page | 1 |
| Ending Page | 40 |
| Page Count | 40 |
| File Format | |
| ISSN | 10468188 |
| e-ISSN | 15582868 |
| DOI | 10.1145/2948063 |
| Volume Number | 35 |
| Issue Number | 1 |
| Journal | ACM Transactions on Information Systems (TOIS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-09-03 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Community question answering Yahoo Answers Best answer prediction Distributional semantics Expert finding Expertise networks |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Information Systems Business, Management and Accounting |
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