Aspect Term Extraction Based on MFE-CRF
This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to imp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Information |
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| Online Access: | http://www.mdpi.com/2078-2489/9/8/198 |
| _version_ | 1819111505005641728 |
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| author | Yanmin Xiang Hongye He Jin Zheng |
| author_facet | Yanmin Xiang Hongye He Jin Zheng |
| author_sort | Yanmin Xiang |
| collection | DOAJ |
| description | This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex. |
| first_indexed | 2024-12-22T03:58:40Z |
| format | Article |
| id | doaj.art-56b0949f3cc7479ba220cda7451fd68d |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-22T03:58:40Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-56b0949f3cc7479ba220cda7451fd68d2022-12-21T18:39:48ZengMDPI AGInformation2078-24892018-08-019819810.3390/info9080198info9080198Aspect Term Extraction Based on MFE-CRFYanmin Xiang0Hongye He1Jin Zheng2School of Information Science and Engineering, Central South University, Changsha 410000, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410000, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410000, ChinaThis paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.http://www.mdpi.com/2078-2489/9/8/198sentiment analysisaspect term extractionMFECRFkmeans++ algorithm |
| spellingShingle | Yanmin Xiang Hongye He Jin Zheng Aspect Term Extraction Based on MFE-CRF Information sentiment analysis aspect term extraction MFE CRF kmeans++ algorithm |
| title | Aspect Term Extraction Based on MFE-CRF |
| title_full | Aspect Term Extraction Based on MFE-CRF |
| title_fullStr | Aspect Term Extraction Based on MFE-CRF |
| title_full_unstemmed | Aspect Term Extraction Based on MFE-CRF |
| title_short | Aspect Term Extraction Based on MFE-CRF |
| title_sort | aspect term extraction based on mfe crf |
| topic | sentiment analysis aspect term extraction MFE CRF kmeans++ algorithm |
| url | http://www.mdpi.com/2078-2489/9/8/198 |
| work_keys_str_mv | AT yanminxiang aspecttermextractionbasedonmfecrf AT hongyehe aspecttermextractionbasedonmfecrf AT jinzheng aspecttermextractionbasedonmfecrf |