An unsupervised approach for learning a Chinese IS-A taxonomy from an unstructured corpus

Huang, Shucai; Luo, Xiangfeng; Huang, Jing; Guo, Yike; Gu, Shengwei

doi:10.1016/j.knosys.2019.07.032

Cited by 13 publications

(7 citation statements)

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“…The pattern-based approach was first proposed by Hearst [34]. Such an approach uses predefined lexical patterns (e.g., N 1 such as N 2 , where N 1 and N 2 denote nouns or noun phrases) to acquire hyponyms [6]. Following [34], some researchers employed pattern-based approaches to discover synonym sets from text.…”

Section: Pattern-based Approachesmentioning

confidence: 99%

“…Mining entity synonym set is an important task for many entity-based downstream applications, such as knowledge graph construction [1][2][3][4], taxonomy learning [5][6][7][8], and question answering [9][10][11]. An entity synonym set usually contains several different strings representing an identical entity [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Chinese has no specific tenses and voices and no distinction between singular and plural forms. In addition, the word order of Chinese is significantly different from that of English, and there are no spaces between words in Chi-nese [6]. Therefore, the aforementioned approaches are not necessarily suitable for expanding entity synonym sets from Chinese text and have the following limitations:…”

Section: Introductionmentioning

confidence: 99%

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A bilateral context and filtering strategy-based approach to Chinese entity synonym set expansion

Huang

Xiu

et al. 2023

Complex Intell. Syst.

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Entity synonyms play a significant role in entity-based tasks. Previous approaches use linguistic syntax, distributional, and semantic features to expand entity synonym sets from text corpora. Due to the flexibility and complexity of the Chinese language expression, the aforementioned approaches are still difficult to expand entity synonym sets robustly from Chinese text, because these approaches fail to track holistic semantics among entities and suffer from error propagation. This paper introduces an approach for expanding Chinese entity synonym sets based on bilateral context and filtering strategy. Specifically, the approach consists of two novel components. First, a bilateral-context-based Siamese network classifier is proposed to determine whether a new entity should be inserted into the existing entity synonym set. The classifier tracks the holistic semantics of bilateral contexts and is capable of imposing soft holistic semantic constraints to improve synonym prediction. Second, a filtering-strategy-based set expansion algorithm is presented to generate Chinese entity synonym sets. The filtering strategy enhances semantic and domain consistencies to filter out wrong synonym entities, thereby mitigating error propagation. Experimental results on two Chinese real-world datasets demonstrate that the proposed approach is effective and outperforms the selected existing state-of-the-art approaches to the Chinese entity synonym set expansion task.

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Section: Pattern-based Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A bilateral context and filtering strategy-based approach to Chinese entity synonym set expansion

Huang

Xiu

et al. 2023

Complex Intell. Syst.

Self Cite

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“…For a given sentence, extract the noun pair and the pattern from the sentence, and then determine if the taxonomic relation between the nouns matches the relations allowed by the pattern. This formulation is motivated by previous work in taxonomy construction that relied on various approaches ranging from pattern-based methods and syntactic features to word embeddings (Huang et al, 2019;Luu et al, 2016;Roller et al, 2018). As promising as this approach sounds for PreTENS, it involves manual labeling of the noun-pair taxonomic relations in the training set, as we are not allowed to use resources such as WordNet (Fellbaum, 1998) or BabelNet (Navigli and Ponzetto, 2012).…”

Section: Arxiv:221003378v1 [Cscl] 7 Oct 2022 2 Backgroundmentioning

confidence: 99%

UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation

Sarhan¹,

Mosteiro²,

Spruit³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence. For sub-task 1-binary classification-we propose an effective way to enhance the robustness and the generalizability of language models for better classification on this downstream task. We design a two-stage fine-tuning procedure on the ELECTRA language model using data augmentation techniques. Rigorous experiments are carried out using multi-task learning and data-enriched fine-tuning. Experimental results demonstrate that our proposed model, UU-Tax, is indeed able to generalize well for our downstream task. For sub-task 2regression-we propose a simple classifier that trains on features obtained from Universal Sentence Encoder (USE). In addition to describing the submitted systems, we discuss other experiments that employ pre-trained language models and data augmentation techniques. For both sub-tasks, we perform error analysis to further understand the behaviour of the proposed models. We achieved a global F1 Binary score of 91.25% in sub-task 1 and a rho score of 0.221 in sub-task 2. 1

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“…For example, United States USA o § © ¨• ¹ Şyn is a pair of entity synonymous relation, since the "United States" and the "'USA" both represent the same country: The "United States of America." In the specific applications, entity synonymous relations play an important role in many entity-based tasks, such as taxonomy construction (Abu-Salih et al, 2018;Huang et al, 2019;Wang et al, 2019), document retrieval (Kong et al, 2019;Liu et al, 2016;Wongthongtham et al, 2018;Yin et al, 2016), and topic detection (Padmanabhanet al, 2017;Xie et al, 2015). Therefore, extracting entity synonymous relations automatically is a crucial work for many downstream applications.…”

Section: Introductionmentioning

confidence: 99%

Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Yan

Huang

Kong

2021

International Journal of Information Technology and Web Engineering

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Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.

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An unsupervised approach for learning a Chinese IS-A taxonomy from an unstructured corpus

Cited by 13 publications

References 38 publications

A bilateral context and filtering strategy-based approach to Chinese entity synonym set expansion

A bilateral context and filtering strategy-based approach to Chinese entity synonym set expansion

UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation

Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

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