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| Content Provider | ACM Digital Library |
|---|---|
| Author | Joshi, Sachindra Mukherjee, Subhabrata Ajmera, Jitendra |
| Abstract | In this work we propose an unsupervised framework to construct a shallow domain ontology from corpus. It is essential for Information Retrieval systems, Question-Answering systems, Dialogue etc. to identify important concepts in the domain and the relationship between them. We identify important domain terms of which multi-words form an important component. We show that the incorporation of multi-words improves parser performance, resulting in better parser output, which improves the performance of an existing Question-Answering system by upto 7%. On manually annotated smartphone dataset, the proposed system identifies 40:87% of the domain terms, compared to 22% recall obtained using WordNet, 43:77% by Yago and 53:74% by BabelNet respectively. However, it does not use any manually annotated resource like the compared systems. Thereafter, we propose a framework to construct a shallow ontology from the discovered domain terms by identifying four domain relations namely, Synonyms ('similar-to'), Type-Of ('is-a'), Action-On ('methods') and Feature-Of ('attributes'), where we achieve significant performance improvement over WordNet, BabelNet and Yago without using any mode of supervision or manual annotation. |
| Starting Page | 929 |
| Ending Page | 938 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450325981 |
| DOI | 10.1145/2661829.2662087 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Ontologies Knowledge extraction |
| Content Type | Text |
| Resource Type | Article |
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