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Understanding the effects of negative (and positive) pointwise mutual information on word vectors
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Assessing idiomaticity representations in vector models with a noun compound dataset labeled at type and token levels
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AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models
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CogNLP-Sheffield at CMCL 2021 Shared Task: Blending cognitively inspired features with transformer-based language models for predicting eye tracking patterns
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Investigating language impact in bilingual approaches for computational language documentation
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Unsupervised compositionality prediction of nominal compounds
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A dual-attention hierarchical recurrent neural network for dialogue act classification
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When the whole is greater than the sum of its parts : multiword expressions and idiomaticity
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Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
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Empirical evaluation of sequence-to-sequence models for word discovery in low-resource settings
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Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks
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A corpus study of verbal multiword expressions in Brazilian Portuguese
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Unwritten languages demand attention too! Word discovery with encoder-decoder models
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Restricted recurrent neural tensor networks: Exploiting word frequency and compositionality
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UFRGS&LIF at SemEval-2016 task 10: Rule-based MWE identification and predominant-supersense tagging
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Abstract:
This paper presents our approach towards the SemEval-2016 Task 10 - Detecting Minimal Semantic Units and their Meanings. Systems are expected to provide a representation of lexical semantics by (1) segmenting tokens into words and multiword units and (2) providing a supersense tag for segments that function as nouns or verbs. Our pipeline rule-based system uses no external resources and was implemented using the mwetoolkit. First, we extract and filter known MWEs from the training corpus. Second, we group input tokens of the test corpus based on this lexicon, with special treatment for non-contiguous expressions. Third, we use an MWE-aware predominant-sense heuristic for supersense tagging. We obtain an F-score of 51.48% for MWE identification and 49.98% for supersense tagging.
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URL: http://eprints.whiterose.ac.uk/153561/
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How naked is the naked truth? A multilingual lexicon of nominal compound compositionality
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