Challenge: Sememes are the minimum semantic units of natural languages, but their use is limited by a lack of available sememe knowledge bases.
Approach: They propose to use sense alignment to connect BabelNet with HowNet by relaxing constraints until a complete alignment is achieved.
Outcome: The proposed method improves on previous supervised methods by 12% . it is based on interpretable propagation of sememe information between lexical resources .

Similar Papers

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)

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Challenge: Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes.
Approach: They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes .
Outcome: The proposed model outperforms previous methods in terms of MAP and F1 scores.
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)

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Challenge: Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding.
Approach: They propose to use sense-annotated corpora for supervised Word Sense Disambiguation.
Outcome: The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available.
Language Modeling with Sparse Product of Sememe Experts (D18-1)

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Challenge: Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words.
Approach: They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics .
Outcome: Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM.
Automatic Construction of Sememe Knowledge Bases via Dictionaries (2021.findings-acl)

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Challenge: Sememe knowledge bases (SKBs) are used to analyze natural language processing.
Approach: They propose a method to build sememe knowledge bases from an existing dictionary . they propose to use existing dictionaries to build an English and a French SKB .
Outcome: The proposed method is superior to HowNet, the most widely used SKB that takes decades to build manually.
Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)

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Challenge: Existing methods of lexical sememe prediction rely on external context information of words to represent meaning.
Approach: They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information.
Outcome: The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words.
Cross-lingual Lexical Sememe Prediction (D18-1)

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Challenge: Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems .
Approach: They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.
Outcome: The proposed model improves on baseline methods on real-world datasets.
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)

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Challenge: a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources .
Approach: They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment .
Outcome: The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language.
Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction (2021.findings-emnlp)

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Challenge: Sememes are defined as the atomic units to describe the semantic meaning of concepts.
Approach: They propose a method which incorporates internal Chinese character information to help sememe prediction.
Outcome: The proposed method outperforms existing non-external information models on howNet, a famous sememe knowledge base.
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)

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Challenge: Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness.
Approach: They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory.
Outcome: The proposed method achieves significantly better performance than baseline methods.
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (2021.emnlp-main)

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Challenge: Existing methods for cross-lingual entity alignment rely on lexical matching and probability reasoning, but they inherit poor interpretability and low efficiency from neural networks.
Approach: They propose a simple but effective unsupervised entity alignment method without neural networks that can be used to find the equivalent entities between crosslingual KGs.
Outcome: Extensive experiments show that the proposed method beats advanced supervised methods across all datasets while having high efficiency, interpretability, and stability.

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