AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information
Abstract Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric ta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00742-5 |
| _version_ | 1797863234591522816 |
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| author | Mingjing Tang Tong Li Wei Gao Yu Xia |
| author_facet | Mingjing Tang Tong Li Wei Gao Yu Xia |
| author_sort | Mingjing Tang |
| collection | DOAJ |
| description | Abstract Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain. |
| first_indexed | 2024-04-09T22:32:17Z |
| format | Article |
| id | doaj.art-ea8c5ad1cafd44f492aacd8f32723dec |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T22:32:17Z |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-ea8c5ad1cafd44f492aacd8f32723dec2023-03-22T12:43:45ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-06-0191253910.1007/s40747-022-00742-5AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation informationMingjing Tang0Tong Li1Wei Gao2Yu Xia3School of Life Sciences, Yunnan Normal UniversitySchool of Big Data, Yunnan Agricultural UniversitySchool of Information, Yunnan Normal UniversitySchool of Information, Yunnan Normal UniversityAbstract Software knowledge community contains a large scale of software knowledge entity information, complex structure and rich semantic correlations. It is significant to recognize and extract software knowledge entity from software knowledge community, as it has great impact on entity-centric tasks such as software knowledge graph construction, software document generation and expert recommendation. Since the texts of the software knowledge community are unstructured by user-generated texts, it is difficult to apply the traditional entity extraction method in the domain of the software knowledge community due to the problems of entity variation, entity sparsity, entity ambiguity, out-of-vocabulary (OOV) words and the lack of annotated data sets. This paper proposes a novel software knowledge entity extraction model, named AttenSy-SNER, which integrates syntactic features and semantic augmentation information, to extract fine-grained software knowledge entities from unstructured user-generated content. The input representation layer utilizes Bidirectional Encoder Representations from Transformers (BERT) model to extract the feature representation of the input sequence. The contextual coding layer leverages the Bidirectional Long Short-Term Memory (BiLSTM) network and Graph Convolutional Network (GCN) for contextual information and syntactic dependency information, and a semantic augmentation strategy based on attention mechanism is introduced to enrich the semantic feature representation of sequences as well. The tag decoding layer leverages Conditional Random Fields (CRF) to solve the dependency between the output tags and obtain the global optimal label sequence. The results of model comparison experiments show that the proposed model has better performance than the benchmark model in software engineering domain.https://doi.org/10.1007/s40747-022-00742-5Entity extractionSoftware knowledge graphAttention mechanismSyntactic dependency analysisSemantic augmentation |
| spellingShingle | Mingjing Tang Tong Li Wei Gao Yu Xia AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information Complex & Intelligent Systems Entity extraction Software knowledge graph Attention mechanism Syntactic dependency analysis Semantic augmentation |
| title | AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information |
| title_full | AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information |
| title_fullStr | AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information |
| title_full_unstemmed | AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information |
| title_short | AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information |
| title_sort | attensy sner software knowledge entity extraction with syntactic features and semantic augmentation information |
| topic | Entity extraction Software knowledge graph Attention mechanism Syntactic dependency analysis Semantic augmentation |
| url | https://doi.org/10.1007/s40747-022-00742-5 |
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