Semantic Sequential Query Expansion for Biomedical Article Search
The conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot to...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8424166/ |
| _version_ | 1819163106488614912 |
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| author | Fan Fang Bo-Wen Zhang Xu-Cheng Yin |
| author_facet | Fan Fang Bo-Wen Zhang Xu-Cheng Yin |
| author_sort | Fan Fang |
| collection | DOAJ |
| description | The conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot topic in IR research. However, a few research have been conducted on combining semantic and sequence information together. Hence, we propose the semantic sequential dependence model in this paper, which provides an innovative combination of semantic information and the conventional SDM. Specifically, our synonyms are obtained automatically through the word embeddings which are trained on the domain-specific corpus by selecting an appropriate language model. Then, these synonyms are utilized to generate possible sequences with the same semantics as the original query and these sequences are fed into SDM to obtain the final retrieval results. The proposed approach is evaluated on 2016 and 2017 BioASQ benchmark test sets and the experimental results show that our query expansion approach outperforms the baseline and other participants in the BioASQ competitions. |
| first_indexed | 2024-12-22T17:38:51Z |
| format | Article |
| id | doaj.art-a11f7052ef6f4a799a5f8647208d85e2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:38:51Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a11f7052ef6f4a799a5f8647208d85e22022-12-21T18:18:26ZengIEEEIEEE Access2169-35362018-01-016454484545710.1109/ACCESS.2018.28618698424166Semantic Sequential Query Expansion for Biomedical Article SearchFan Fang0https://orcid.org/0000-0003-0382-1990Bo-Wen Zhang1Xu-Cheng Yin2Department of Computer Science, University of Science and Technology Beijing, Beijing, ChinaDepartment of Computer Science, University of Science and Technology Beijing, Beijing, ChinaDepartment of Computer Science, University of Science and Technology Beijing, Beijing, ChinaThe conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot topic in IR research. However, a few research have been conducted on combining semantic and sequence information together. Hence, we propose the semantic sequential dependence model in this paper, which provides an innovative combination of semantic information and the conventional SDM. Specifically, our synonyms are obtained automatically through the word embeddings which are trained on the domain-specific corpus by selecting an appropriate language model. Then, these synonyms are utilized to generate possible sequences with the same semantics as the original query and these sequences are fed into SDM to obtain the final retrieval results. The proposed approach is evaluated on 2016 and 2017 BioASQ benchmark test sets and the experimental results show that our query expansion approach outperforms the baseline and other participants in the BioASQ competitions.https://ieeexplore.ieee.org/document/8424166/Sequential dependence modelsemantic query expansionsemantic sequential dependence modelword embedding |
| spellingShingle | Fan Fang Bo-Wen Zhang Xu-Cheng Yin Semantic Sequential Query Expansion for Biomedical Article Search IEEE Access Sequential dependence model semantic query expansion semantic sequential dependence model word embedding |
| title | Semantic Sequential Query Expansion for Biomedical Article Search |
| title_full | Semantic Sequential Query Expansion for Biomedical Article Search |
| title_fullStr | Semantic Sequential Query Expansion for Biomedical Article Search |
| title_full_unstemmed | Semantic Sequential Query Expansion for Biomedical Article Search |
| title_short | Semantic Sequential Query Expansion for Biomedical Article Search |
| title_sort | semantic sequential query expansion for biomedical article search |
| topic | Sequential dependence model semantic query expansion semantic sequential dependence model word embedding |
| url | https://ieeexplore.ieee.org/document/8424166/ |
| work_keys_str_mv | AT fanfang semanticsequentialqueryexpansionforbiomedicalarticlesearch AT bowenzhang semanticsequentialqueryexpansionforbiomedicalarticlesearch AT xuchengyin semanticsequentialqueryexpansionforbiomedicalarticlesearch |