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Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models
Jey Han Lau
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Paul Cook
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Diana McCarthy
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Spandana Gella
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Timothy Baldwin
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Paper Details:
Month: June
Year: 2014
Location: Baltimore, Maryland
Venue:
ACL |
Citations
URL
Evaluating a Topic Modelling Approach to Measuring Corpus Similarity
Richard Fothergill
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Paul Cook
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Timothy Baldwin
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Learning Kernels for Semantic Clustering: A Deep Approach
Ignacio Arroyo-Fernández
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LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning
Andrew Bennett
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Timothy Baldwin
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Jey Han Lau
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Diana McCarthy
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Francis Bond
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A Bayesian Model of Diachronic Meaning Change
Lea Frermann
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Mirella Lapata
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VUA-background : When to Use Background Information to Perform Word Sense Disambiguation
Marten Postma
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Ruben Izquierdo
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Piek Vossen
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Determining the most frequent senses using Russian linguistic ontology RuThes
Natalia Loukachevitch
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Ilia Chetviorkin
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A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations
Waseem Gharbieh
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Virendra Bhavsar
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Paul Cook
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Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
Binny Mathew
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Suman Kalyan Maity
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Pratip Sarkar
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Animesh Mukherjee
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Pawan Goyal
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http://webdocs.cs
http://www.macmillandictionary.com/
Field Of Study
Task
Language Understanding
Language Identification
Tagging
Word Sense Disambiguation
Semantic Similarity
Approach
Topic Modeling
Generative Model
Language
English
Malay
Dataset
News
Social Media
Twitter
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