mlsanityData to 7 Oct 2026

An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex

arXiv 2203.15173 ↗First posted 29 Mar 2022cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a word embedding model facilitates subjecting legal documents to machine learning, deep learning, and other algorithms and subsequently performing the downstream tasks of natural language processing vis-`a-vis, for instance, document classification, contract review, and machine translation. The most common and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation. This paper proposes establishing a 1,134 Legal Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be ubiquitous in the word embedding model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 29 Mar 2022.

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References · 14

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2019Inducing Relational Knowledge from BERTcited by 75
  4. 2018Analogical Reasoning on Chinese Morphological and Semantic Relationscited by 50
  5. 2019Evaluating Word Embedding Models: Methods and Experimental Resultscited by 37
  6. 2015Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learningcited by 29
  7. 2018A Survey of Word Embeddings Evaluation Methodscited by 26
  8. 2017Specialising Word Vectors for Lexical Entailmentcited by 18
  9. 2014Radical-Enhanced Chinese Character Embeddingcited by 9
  10. 2015Component-Enhanced Chinese Character Embeddingscited by 8
  11. 2017Learning Chinese Word Representations From Glyphs Of Characterscited by 8
  12. 2016Context-Dependent Word Representation for Neural Machine Translationcited by 7
2 more
  1. 2020RethinkCWS: Is Chinese Word Segmentation a Solved Task?cited by 7
  2. 2019Relational Word Embeddingscited by 2

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