Inducing a Semantically Rich Nested Event Model
Research has revealed that getting data with named entities (NEs) labels are laboured intensive and costly. This paper is proposing two approaches to enable NE classes to be added to the semantic role label (SRL) predicateargument structure of Nested Event Model. The first approach associates SRL...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
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Springer International Publishing
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/10636/ http://ir.unimas.my/id/eprint/10636/1/NO%2070%20Inducing%20a%20semantically%20rich%20nested%20event%20model%20%28abstract%29.pdf |
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| author | Nyuk, Hiong Siaw Bali, Ranaivo-Malançon Narayanan, Kulathuramaiyer Jane, Labadin |
| author_facet | Nyuk, Hiong Siaw Bali, Ranaivo-Malançon Narayanan, Kulathuramaiyer Jane, Labadin |
| author_sort | Nyuk, Hiong Siaw |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Research has revealed that getting data with named entities (NEs)
labels are laboured intensive and costly. This paper is proposing two approaches
to enable NE classes to be added to the semantic role label (SRL) predicateargument
structure of Nested Event Model. The first approach associates SRL to
Named Entity Recognition (NER), which is named as SRL-NER, to tag the
appropriate entity class to the simple argument of the model. The second
approach associates SRL to NER by fine-tuning entities in complex argument
structures with Automatic Content Extraction (ACE) structure. This approach is
called SRL-ACE-NER. Stanford NER tool is used as the benchmark for evaluation.
The result shows that the proposed approaches are able to recognize
more PERSON entities. However, the approaches are not able to recognize
LOCATION/PLACE as efficiently as the benchmark. It is also observed that the
benchmark tool is sometimes not able to tag as comprehensively as the proposed
approaches. This paper has successfully demonstrated the potential of using a
semantically enriched Nested Event Model as an alternative for NER technique.
SRL-ACE-NER has achieved an average precision of 92 % in recognising
PERSON, LOCATION/PLACE, TIME, and ORGANIZATION. |
| first_indexed | 2025-11-15T06:29:54Z |
| format | Book Chapter |
| id | unimas-10636 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:29:54Z |
| publishDate | 2015 |
| publisher | Springer International Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-106362016-10-24T06:17:20Z http://ir.unimas.my/id/eprint/10636/ Inducing a Semantically Rich Nested Event Model Nyuk, Hiong Siaw Bali, Ranaivo-Malançon Narayanan, Kulathuramaiyer Jane, Labadin T Technology (General) Research has revealed that getting data with named entities (NEs) labels are laboured intensive and costly. This paper is proposing two approaches to enable NE classes to be added to the semantic role label (SRL) predicateargument structure of Nested Event Model. The first approach associates SRL to Named Entity Recognition (NER), which is named as SRL-NER, to tag the appropriate entity class to the simple argument of the model. The second approach associates SRL to NER by fine-tuning entities in complex argument structures with Automatic Content Extraction (ACE) structure. This approach is called SRL-ACE-NER. Stanford NER tool is used as the benchmark for evaluation. The result shows that the proposed approaches are able to recognize more PERSON entities. However, the approaches are not able to recognize LOCATION/PLACE as efficiently as the benchmark. It is also observed that the benchmark tool is sometimes not able to tag as comprehensively as the proposed approaches. This paper has successfully demonstrated the potential of using a semantically enriched Nested Event Model as an alternative for NER technique. SRL-ACE-NER has achieved an average precision of 92 % in recognising PERSON, LOCATION/PLACE, TIME, and ORGANIZATION. Springer International Publishing 2015 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/10636/1/NO%2070%20Inducing%20a%20semantically%20rich%20nested%20event%20model%20%28abstract%29.pdf Nyuk, Hiong Siaw and Bali, Ranaivo-Malançon and Narayanan, Kulathuramaiyer and Jane, Labadin (2015) Inducing a Semantically Rich Nested Event Model. In: Intelligent Software Methodologies, Tools and Techniques. Communications in Computer and Information Science, 513 . Springer International Publishing, pp. 361-375. ISBN 978-3-319-17530-0 http://link.springer.com/chapter/10.1007%2F978-3-319-17530-0_25 10.1007/978-3-319-17530-0_25 |
| spellingShingle | T Technology (General) Nyuk, Hiong Siaw Bali, Ranaivo-Malançon Narayanan, Kulathuramaiyer Jane, Labadin Inducing a Semantically Rich Nested Event Model |
| title | Inducing a Semantically Rich Nested Event Model |
| title_full | Inducing a Semantically Rich Nested Event Model |
| title_fullStr | Inducing a Semantically Rich Nested Event Model |
| title_full_unstemmed | Inducing a Semantically Rich Nested Event Model |
| title_short | Inducing a Semantically Rich Nested Event Model |
| title_sort | inducing a semantically rich nested event model |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/10636/ http://ir.unimas.my/id/eprint/10636/ http://ir.unimas.my/id/eprint/10636/ http://ir.unimas.my/id/eprint/10636/1/NO%2070%20Inducing%20a%20semantically%20rich%20nested%20event%20model%20%28abstract%29.pdf |