NERSE: Named Entity Recognition in Software Engineering as a Service

Reddy, M. Veera Prathap; Prasad, P. V. R. D.; Chikkamath, Manjunath; Mandadi, Sarathchandra

doi:10.1007/978-3-030-32242-7_6

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Existing researches [2][3][4][5][6][7][8] are mostly based on pipeline method, and entity extraction and relation extraction are modeled as two independent tasks. The model implementation of these methods is relatively simple and flexible, but the interaction and association of entity extraction and relation extraction are ignored, causing many problems [9].…”

Section: Introductionmentioning

confidence: 99%

SCL-SKG: software knowledge extraction with span-level contrastive learning

Tang,

Zhang,

Zheng

et al. 2023

Artif Intell Rev

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The text of software knowledge community contains abundant knowledge of software engineering field. The software knowledge triplet can be extracted automatically and efficiently to form the software knowledge graph, which is helpful for software knowledge-centric intelligent applications, such as intelligent question answering, automatic document generation and software expert recommendation. Most existing methods are confronted with problems of task dependence and entity overlap. In this paper, we propose a software knowledge triplet extraction method based on span-level contrastive learning. From the level of sentence sequence modelling, we model the sentence sequence with span as a unit, and generate abundant positive and negative samples of entity span through the span representation layer to avoid the problem that the token-level method cannot select overlapping entities. From the level of feature learning, we propose supervised entity contrastive learning and relation contrastive learning, which obtain enhanced feature representation of entity span and entity pair through positive and negative sample enhancement and contrastive loss function construction.Experiments are conducted on the dataset which is constructed based on texts of the StackOverflow, and show that our approach achieves a better performance than baseline models.

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Section: Introductionmentioning

confidence: 99%

SCL-SKG: software knowledge extraction with span-level contrastive learning

Tang,

Zhang,

Zheng

et al. 2023

Artif Intell Rev

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Aspect Based Sentiment Analysis – An Incremental Model Learning Approach Using LSTM-RNN

Londhe

Rao

2021

Communications in Computer and Information Science

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AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information

Tang

Gao

et al. 2022

Complex Intell. Syst.

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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.

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NERSE: Named Entity Recognition in Software Engineering as a Service

Cited by 4 publications

References 27 publications

SCL-SKG: software knowledge extraction with span-level contrastive learning

SCL-SKG: software knowledge extraction with span-level contrastive learning

Aspect Based Sentiment Analysis – An Incremental Model Learning Approach Using LSTM-RNN

AttenSy-SNER: software knowledge entity extraction with syntactic features and semantic augmentation information

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