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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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Abstract:
The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs. We explore dense vector representations as an effective way to approximate the same information: we introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g.the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks and show evaluations on the WordNet graph and two knowledge base graphs. ... : In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL'2019). Florence, Italy ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1906.07040 https://dx.doi.org/10.48550/arxiv.1906.07040
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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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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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