Papers by Carlos Ramisch
In the LLM era, Word Sense Induction remains unsolved (2025.findings-acl)
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| Challenge: | Existing word sense induction methods rely on predefined sense inventories of arbitrary granularity and require labor intensive sense-annotated data. |
| Approach: | They propose to evaluate an LLM-based word sense induction method for English . they use data augmentation sources, corpus and lexicon, and semi-supervised scenarios to augment the data. |
| Outcome: | The proposed method surpasses the previous SOTA system on a English set by 3.3%. |
Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)
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| Challenge: | Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing . |
| Approach: | They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser. |
| Outcome: | The proposed model can be trained on 40 languages with the help of typological features. |
Injecting Wiktionary to improve token-level contextual representations using contrastive learning (2024.eacl-short)
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| Challenge: | lexical semantics tasks require contextual word embeddings that are not blind to context, despite the fact that vectors of the same meaning are too different. |
| Approach: | They propose to fine-tune pre-trained language models by using automatically self-augmented examples to target contextual word embeddings. |
| Outcome: | The proposed method achieves significant improvements on the original WiC test set and in two new tests. |
SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings (2020.coling-main)
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| Challenge: | Contextualised embeddings are a key component of human languages but their opaqueness makes it difficult to interpret their behaviour. |
| Approach: | They propose a weakly supervised method to learn interpretable embeddings from raw corpora and seed words. |
| Outcome: | The proposed model can represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. |
Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (2020.coling-main)
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| Challenge: | Multiword expressions (MWEs) are word combinations idiosyncratic with respect to syntax or semantics. |
| Approach: | They propose to use a language-independent system to identify previously seen VMWEs by combining filters to obtain the best averaged F-score over 11 languages and the best score for both seen and unseen VMwes. |
| Outcome: | The proposed system obtains the best averaged F-score over 11 languages and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMwes in texts. |
Evaluating Pretrained Causal Language Models for Synonymy (2025.findings-acl)
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| Challenge: | Despite the scaling of causal language models, the underlying basis of complex skills remains unclear. |
| Approach: | They propose that subjacent skills such as synonymy might be explained using linguistic concepts. |
| Outcome: | The proposed model recognizes synonymy but struggles to generate synonyms when prompted with relevant context. |
If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions (C18-1)
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| Challenge: | Multiword expressions (VMWEs) show idiosyncratic variability, which is challenging for NLP applications. |
| Approach: | They propose to use a model to identify variants of previously seen VMWEs by comparing VMWAs with morpho-syntactic variations. |
| Outcome: | The proposed approach outperforms a baseline by 4 percent points of F-measure on a French corpus. |
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. |
| Approach: | They propose a new architecture for incorporating multiword expression features into tweets to improve their accuracy. |
| Outcome: | The proposed system outperforms existing systems on the hate speech detection task on English Twitter. |
Towards a Variability Measure for Multiword Expressions (N18-2)
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| Challenge: | Multiword expressions (MWEs) are groups of words whose meaning does not derive from the meaning of their components and from their syntactic structure in a regular way. |
| Approach: | They propose to use a language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues to assess its relevance with respect to a linguistic benchmark. |
| Outcome: | The proposed measure is useful for VMWE classification and variant identification on a French corpus. |