SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |
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| Challenge: | Conventionally, lexical relations in word vector space have been defined by collections of relatively consistent relationships, or vector offsets, between word-pairs. |
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| Challenge: | Existing methods to specialize distributional vectors to better reflect a particular relation are lacking in modern natural language processing. |
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Within-Between Lexical Relation Classification (2020.emnlp-main)
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| Challenge: | Existing methods for recognizing lexical-semantic relations between words are path-based and distributional. |
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AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
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Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
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Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space (D18-1)
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