Automatic Construction of Sememe Knowledge Bases via Dictionaries

Qi, Fanchao; Chen, Yangyi; Feng-yu, Wang; Liu, Zhiyuan; Chen, Xiao; Sun, Maosong

doi:10.18653/v1/2021.findings-acl.411

Cited by 5 publications

(3 citation statements)

References 55 publications

(31 reference statements)

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“…Several studies concerning the construction of SKBs have also been carried out. Qi et al (2021) pioneered the formulation of a method involving the utilization of dictionaries for sememe knowledge base creation. The authors applied this entirely automatic method to create two sememe knowledge bases, respectively concerning the English and French languages.…”

Section: Knowledge Representation and Semi-automatic Semic Analysismentioning

confidence: 99%

A Novel Approach to Semic Analysis: Extraction of Atoms of Meaning to Study Polysemy and Polyreferentiality

Bonato,

Di Nunzio,

Vezzani

2024

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Semic analysis is a linguistic technique aimed at methodically factorizing the meaning of terms into a collection of minimum non-decomposable atoms of meaning. In this study, we propose a methodology targeted at enhancing the systematicity of semic analysis of medical terminology in order to increase the quality of the creation of the set of atoms of meaning and improve the identification of concepts, as well as enhance specialized domain studies. Our approach is based on: (1) a semi-automatic domain-specific corpus-based extraction of semes, (2) the application of the property of termhood to address the diaphasic and the diastratic variations of language, (3) the automatic lemmatization of semes, and (4) seme weighting to establish the order of semes in the sememe. The paper explores the distinction between denotative and connotative semes, offering insights into polysemy and polyreferentiality in medical terminology.

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Section: Knowledge Representation and Semi-automatic Semic Analysismentioning

confidence: 99%

A Novel Approach to Semic Analysis: Extraction of Atoms of Meaning to Study Polysemy and Polyreferentiality

Bonato,

Di Nunzio,

Vezzani

2024

Languages

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“…Moreover, a recent work tries to construct a sememe KB on the basis of a dictionary (Qi et al, 2021a). It does not rely on the existing sememe annotations of HowNet or use the sememe set of HowNet.…”

Section: Sememe Knowledge Basementioning

confidence: 99%

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

Qi¹,

Lv²,

Liu³

et al. 2022

Preprint

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In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks. However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. To address this issue, the task of sememe prediction for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically predicting sememes for a BabelNet synset, the words in many languages in the synset would obtain sememe annotations simultaneously. However, previous SPBS methods have not taken full advantage of the abundant information in BabelNet. In this paper, we utilize the multilingual synonyms, multilingual glosses and images in BabelNet for SPBS. We design a multimodal information fusion model to encode and combine this information for sememe prediction. Experimental results show the substantial outperformance of our model over previous methods (about 10 MAP and F1 scores). All the code and data of this paper can be obtained at https: //github.com/thunlp/MSGI.

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“…In addition, Qi et al (2021a) make an attempt to construct an SKB based on a dictionary fully automatically. They regard the words in the controlled defining vocabulary of a dictionary as sememes rather than use the existing sememe set of HowNet.…”

Section: Sememe Knowledge Basementioning

confidence: 99%

Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention

Ye¹,

Qi²,

Liu³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Sememe knowledge bases (SKBs), which annotate words with the smallest semantic units (i.e., sememes), have proven beneficial to many NLP tasks. Building an SKB is very time-consuming and labor-intensive. Therefore, some studies have tried to automate the building process by predicting sememes for the unannotated words. However, all existing sememe prediction studies ignore the hierarchical structures of sememes, which are important in the sememebased semantic description system. In this work, we tackle the structured sememe prediction problem for the first time, which is aimed at predicting a sememe tree with hierarchical structures rather than a set of sememes. We design a sememe tree generation model based on Transformer with an adjusted attention mechanism, which shows its superiority over the baseline methods in experiments. We also conduct a series of quantitative and qualitative analyses of the effectiveness of our model. All the code and data of this paper are available at https://github.com/thunlp/STG.

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Automatic Construction of Sememe Knowledge Bases via Dictionaries

Cited by 5 publications

References 55 publications

A Novel Approach to Semic Analysis: Extraction of Atoms of Meaning to Study Polysemy and Polyreferentiality

A Novel Approach to Semic Analysis: Extraction of Atoms of Meaning to Study Polysemy and Polyreferentiality

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention

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