Abstract
Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to find candidate aliases of a given name. We use anchor texts to design a word co-occurrence model and use it to define various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-count-based association measures using support vector machines to leverage a robust alias detection method. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically significant mean reciprocal rank of 0.6718. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 77-88 |
| Number of pages | 12 |
| Volume | 5221 LNAI |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 6th International Conference on Natural Language Processing, GoTAL 2008 - Gothenburg Duration: 2008 Aug 25 → 2008 Aug 27 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5221 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 6th International Conference on Natural Language Processing, GoTAL 2008 |
|---|---|
| City | Gothenburg |
| Period | 08/8/25 → 08/8/27 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Automatically extracting personal name aliases from the web. / Bollegala, Danushka; Honma, Taiki; Matsuo, Yutaka; Ishizuka, Mitsuru.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5221 LNAI 2008. p. 77-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5221 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatically extracting personal name aliases from the web
AU - Bollegala, Danushka
AU - Honma, Taiki
AU - Matsuo, Yutaka
AU - Ishizuka, Mitsuru
PY - 2008
Y1 - 2008
N2 - Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to find candidate aliases of a given name. We use anchor texts to design a word co-occurrence model and use it to define various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-count-based association measures using support vector machines to leverage a robust alias detection method. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically significant mean reciprocal rank of 0.6718. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.
AB - Extracting aliases of an entity is important for various tasks such as identification of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must first identify those entities. We propose a novel approach to find aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to find candidate aliases of a given name. We use anchor texts to design a word co-occurrence model and use it to define various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-count-based association measures using support vector machines to leverage a robust alias detection method. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically significant mean reciprocal rank of 0.6718. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.
UR - http://www.scopus.com/inward/record.url?scp=52149108487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=52149108487&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85287-2_8
DO - 10.1007/978-3-540-85287-2_8
M3 - Conference contribution
AN - SCOPUS:52149108487
SN - 3540852867
SN - 9783540852865
VL - 5221 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 88
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -