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Embeddings for Word Sense Disambiguation: An Evaluation Study
TLDR
We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. Expand
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SensEmbed: Learning Sense Embeddings for Word and Relational Similarity
TLDR
We propose a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement, reporting state-of-the-art performance on multiple datasets. Expand
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Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities
TLDR
In this paper we put forward a novel multilingual vector representation, called Nasari, which not only enables accurate representation of word senses in different languages, but it also provides two main advantages over existing approaches: (1) high coverage, including both concepts and named entities, and (2) comparability across languages and linguistic levels (i.e., words, senses and concepts). Expand
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Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity
TLDR
We present a unified approach to semantic similarity that operates at multiple levels, all the way from comparing word senses to comparing text documents. Expand
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SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity
TLDR
This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages. Expand
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NASARI: a Novel Approach to a Semantically-Aware Representation of Items
TLDR
The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. Expand
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From senses to texts: An all-in-one graph-based approach for measuring semantic similarity
TLDR
We present a unified graph-based approach for measuring semantic similarity which enables effective comparison of linguistic items at multiple levels, from word senses to full texts. Expand
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SemEval-2014 Task 3: Cross-Level Semantic Similarity
TLDR
This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item,such as a sentence. Expand
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What’s missing in geographical parsing?
TLDR
We evaluate and analyse the performance of state-of-the-art geoparsers on a number of corpora and highlight the challenges in detail. Expand
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SemEval-2016 Task 14: Semantic Taxonomy Enrichment
TLDR
We have introduced SemEval-2016 Task 14 as a framework and dataset for evaluating the accuracy of systems at integrating new definitions as concepts into an ontology using WordNet 3.0. Expand
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