Extracting fields from free-text
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2016
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| Online Access: | http://hdl.handle.net/1721.1/106077 |
| _version_ | 1826189211357675520 |
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| author | Cattori, Pedro |
| author2 | Samuel Madden. |
| author_facet | Samuel Madden. Cattori, Pedro |
| author_sort | Cattori, Pedro |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. |
| first_indexed | 2024-09-23T08:11:16Z |
| format | Thesis |
| id | mit-1721.1/106077 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:11:16Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1060772019-04-09T17:13:10Z Extracting fields from free-text Cattori, Pedro Samuel Madden. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 86-87). The Field Extraction Library (FEL) provides functions for named-entity extraction within free text. FEL models the content structure of the specified named-entities rather than relying on brittle, context-specific separator logic. Users specify the names of the fields they wish to extract, which determine the number of states for an underlying Hidden Markov Model. The observable emission set is pre-determined by FEL's tokenizer. Once the model topology is set, users provide training examples of the form: x = raw text, y {fieldl: val1, field2:val2, ... } FEL learns the parameters of the underlying Hidden Markov Model by maximum likelihood model-estimation on the training examples. FEL is designed to operate on small, sparse training data. As a result, users can provide few (less than 10) training examples to bootstrap the model. FEL offers 3 iterative mechanisms for scaling data quality as users provide guidance through additional feedback: (1) accept more training examples, (2) create landmark states, and (3) bridge related states with state bridges. FEL detects ambiguities both in its internal model and in the extraction results to prompt users for more feedback. Once the model yields acceptable result quality, users can extract fields into a table for easy querying and exporting. by Pedro Cattori. M. Eng. 2016-12-22T16:28:01Z 2016-12-22T16:28:01Z 2016 2016 Thesis http://hdl.handle.net/1721.1/106077 965198310 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 87 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Cattori, Pedro Extracting fields from free-text |
| title | Extracting fields from free-text |
| title_full | Extracting fields from free-text |
| title_fullStr | Extracting fields from free-text |
| title_full_unstemmed | Extracting fields from free-text |
| title_short | Extracting fields from free-text |
| title_sort | extracting fields from free text |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/106077 |
| work_keys_str_mv | AT cattoripedro extractingfieldsfromfreetext |