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Detecting Text Formality: A Study of Text Classification Approaches ...
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Taxonomy Enrichment with Text and Graph Vector Representations ...
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Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates ...
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Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint ...
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RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language ...
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Word Sense Disambiguation for 158 Languages using Word Embeddings Only ...
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Studying Taxonomy Enrichment on Diachronic WordNet Versions ...
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A Comparative Study of Lexical Substitution Approaches based on Neural Language Models ...
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Making Fast Graph-based Algorithms with Graph Metric Embeddings ...
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On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings ...
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Abstract:
The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincaré similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, ... : Accepted in ACL 2019 [Long Paper] ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1906.03007 https://dx.doi.org/10.48550/arxiv.1906.03007
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Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings ...
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Hypernyms extracted from a large text corpus using Hearst lexical-syntactic patterns ...
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Hypernyms extracted from a large text corpus using Hearst lexical-syntactic patterns ...
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
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HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings
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Watset: Local-global graph clustering with applications in sense and frame induction
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RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language ...
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How much does a word weigh? Weighting word embeddings for word sense induction ...
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