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Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates ...
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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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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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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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RUSSE: The First Workshop on Russian Semantic Similarity ...
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An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages ...
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Enriching Frame Representations with Distributionally Induced Senses ...
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Abstract:
We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora. These features are extracted from distributionally induced sense inventories and subsequently linked to the manually-constructed frame representations to boost the performance of frame disambiguation in context. Since Framester is a frame-based knowledge graph, which enables full-fledged OWL querying and reasoning, our resource paves the way for the development of novel, deeper semantic-aware applications that could benefit from the combination of knowledge from text and complex symbolic representations of events and participants. Together with the resource we also provide the software we developed for the evaluation in the task of Word Frame Disambiguation (WFD). ... : In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan. ELRA ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1803.05829 https://dx.doi.org/10.48550/arxiv.1803.05829
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Negative Sampling Improves Hypernymy Extraction Based on Projection Learning ...
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A Framework for Enriching Lexical Semantic Resources with Distributional Semantics ...
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