Incorporating Content Structure into Text Analysis Applications
URL to papers listed on conference site
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computational Linguistics
2011
|
| Online Access: | http://hdl.handle.net/1721.1/62235 https://orcid.org/0000-0002-2921-8201 |
| _version_ | 1826212202317611008 |
|---|---|
| author | Sauper, Christina Joan Haghighi, Aria Barzilay, Regina |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Sauper, Christina Joan Haghighi, Aria Barzilay, Regina |
| author_sort | Sauper, Christina Joan |
| collection | MIT |
| description | URL to papers listed on conference site |
| first_indexed | 2024-09-23T15:17:39Z |
| format | Article |
| id | mit-1721.1/62235 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:17:39Z |
| publishDate | 2011 |
| publisher | Association for Computational Linguistics |
| record_format | dspace |
| spelling | mit-1721.1/622352022-09-29T13:59:27Z Incorporating Content Structure into Text Analysis Applications Sauper, Christina Joan Haghighi, Aria Barzilay, Regina Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Barzilay, Regina Barzilay, Regina Sauper, Christina Joan Haghighi, Aria URL to papers listed on conference site Information about the content structure of a document is largely ignored by current text analysis applications such as information extraction and sentiment analysis. This stands in contrast to the linguistic intuition that rich contextual information should benefit such applications. We present a framework which combines a supervised text analysis application with the induction of latent content structure. Both of these elements are learned jointly using the EM algorithm. The induced content structure is learned from a large unannotated corpus and biased by the underlying text analysis task. We demonstrate that exploiting content structure yields significant improvements over approaches that rely only on local context. 2011-04-19T18:21:46Z 2011-04-19T18:21:46Z 2010-10 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/62235 Sauper, Christina, Aria Haghighi, and Regina Barzilay. "Incorporating Content Structure into Text Analysis Applications." EMNLP 2010: Conference on Empirical Methods in Natural Language Processing, October 9-11, 2010, MIT, Massachusetts, USA. https://orcid.org/0000-0002-2921-8201 en_US http://www.lsi.upc.edu/events/emnlp2010/papers.html EMNLP 2010 : Conference on Empirical Methods in Natural Language Processing Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computational Linguistics MIT web domain |
| spellingShingle | Sauper, Christina Joan Haghighi, Aria Barzilay, Regina Incorporating Content Structure into Text Analysis Applications |
| title | Incorporating Content Structure into Text Analysis Applications |
| title_full | Incorporating Content Structure into Text Analysis Applications |
| title_fullStr | Incorporating Content Structure into Text Analysis Applications |
| title_full_unstemmed | Incorporating Content Structure into Text Analysis Applications |
| title_short | Incorporating Content Structure into Text Analysis Applications |
| title_sort | incorporating content structure into text analysis applications |
| url | http://hdl.handle.net/1721.1/62235 https://orcid.org/0000-0002-2921-8201 |
| work_keys_str_mv | AT sauperchristinajoan incorporatingcontentstructureintotextanalysisapplications AT haghighiaria incorporatingcontentstructureintotextanalysisapplications AT barzilayregina incorporatingcontentstructureintotextanalysisapplications |