| Challenge: | MWTs are motivated combinations that clearly convey the concept they designate. |
| Approach: | They propose a word-embedding-based approach for automatic acquisition of MWT synonyms that manage length variability. |
| Outcome: | The proposed approach improves on two specialized domain corpora and shows that it is more efficient than baseline approaches. |
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| Challenge: | Existing methods for specializing word embeddings rely on external knowledge . we propose a new method that focuses on semantic similarity and synonym extraction without external knowledge. |
| Approach: | They propose a method that exploits pseudo-sense for building several representations for each word and uses these representations to make initial embeddings more generic. |
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Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
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Building Comparable Corpora for Assessing Multi-Word Term Alignment (2022.lrec-1)
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A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches (L18-1)
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| Challenge: | WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them. |
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A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
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Multiplex Word Embeddings for Selectional Preference Acquisition (D19-1)
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Representing Multiword Term Variation in a Terminological Knowledge Base: a Corpus-Based Study (2020.lrec-1)
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| Challenge: | Multiword terms are the most frequent type of lexical units in scientific and technical communication. rendering them in another language is not easy due to their cognitive complexity, proliferation of different forms, and their unsystematic representation in terminographic resources. |
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Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
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Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
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| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
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