Chinese entity attributes extraction based on bidirectional LSTM networks

He, Zhonghe; Zhou, Zhiyong; Gan, Lu; Huang, Jiuming; Zeng, Yan

doi:10.1504/ijcse.2019.096988

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Li et al extracted opinion targets from user-generated content, using a method based on rules and semantic role labelling (Li & Chang, 2019). He et al trained the classifier based on BiLSTM networks to extract Chinese entity attributes (He et al, 2019). The extraction of sentiment elements can also provide data support for sentiment analysis (Dashtipour et al, 2020).…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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“…Machine learning-based techniques include supervised [6,13], semisupervised [14,15], and unsupervised [16,17] models. Lately, deep learning-based techniques include supervised [18,19] and distant supervised [20] models. All these models are relatively easy to build, but with poor portability and extensibility.…”

Section: Features Of Entity Relationship Extractionmentioning

confidence: 99%

A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Pan

et al. 2021

Complexity

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To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.

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“…The model is used to decide which expression is more preferred to achieve the attribute extraction. For the low performance of slot filling method applied in Chinese entity-attribute extraction at present, He et al [26] presented a distant supervision relation extraction method based on bidirectional long short-term memory neural network. Wei et al [27] proposed an attribute extraction-oriented class-convolutional interactive attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

You

Yang

Han³

et al. 2021

Wireless Communications and Mobile Computing

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The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F 1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.

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Chinese entity attributes extraction based on bidirectional LSTM networks

Cited by 9 publications

References 11 publications

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

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