| Challenge: | Existing methods for multiword expression detection are based on sequence labeling and statistical measures. |
| Approach: | They propose a weakly supervised method for multiword expressions extraction . they use a lexicon of English multiword lexical units as a reference knowledge base . |
| Outcome: | The proposed method can be easily applied to other languages. |
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| Challenge: | MUMULS tagger for automatic detection of verbal multiword expressions is based on neural networks . character-level embeddings can improve the performance, reducing out-of-vocabulary rate . multiword Expressions are viewed by computational linguists as a "pain in the neck of NLP" |
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Multilingual Normalization of Temporal Expressions with Masked Language Models (2023.eacl-main)
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| Challenge: | Existing methods for normalizing temporal expressions are rule-based, which severely limits the applicability in multilingual settings. |
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
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| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
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An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification (2020.coling-main)
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| Challenge: | Existing methods for learning multi-word expressions have language sparsity and are not supervised. |
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Identification of Multiword Expressions in Tweets for Hate Speech Detection (2022.lrec-1)
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| Challenge: | Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. |
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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
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Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
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CoAM: Corpus of All-Type Multiword Expressions (2025.acl-long)
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Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing datasets for multiword expressions are inconsistently annotated, limited to a single type of MWE, or limited in size. |
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A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality (L18-1)
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| Challenge: | Existing literature on semantically idiosyncratic multiword expressions is limited to English . idiomatic expressions are phraseological units consisting of more than one lexeme and exhibit some kind of idiom. |
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KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
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