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evision PDF of 'Improving Topic Models with Latent Feature Word Representations
Dat Quoc Nguyen
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Richard Billingsley
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Lan Du
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Mark Johnson
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Paper Details:
Year: 2015
Venue:
TACL |
Citations
URL
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs
Ximing Li
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Jinjin Chi
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Changchun Li
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Jihong Ouyang
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Bo Fu
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Latent Topic Embedding
Di Jiang
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Lei Shi
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Rongzhong Lian
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Hua Wu
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SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
Dheeraj Mekala
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Vivek Gupta
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Bhargavi Paranjape
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Harish Karnick
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Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations
Zhe Zhang
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Munindar Singh
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Generative Topic Embedding: a Continuous Representation of Documents
Shaohua Li
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Tat-Seng Chua
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Jun Zhu
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Chunyan Miao
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Learning Text Pair Similarity with Context-sensitive Autoencoders
Hadi Amiri
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Philip Resnik
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Jordan Boyd-Graber
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Hal Daumé III
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A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings
Weihua Hu
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Jun’ichi Tsujii
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Neural Models for Documents with Metadata
Dallas Card
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Chenhao Tan
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Noah A. Smith
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Neural Sparse Topical Coding
Min Peng
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Qianqian Xie
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Yanchun Zhang
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Hua Wang
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Xiuzhen Zhang
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Jimin Huang
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Gang Tian
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A Mixture Model for Learning Multi-Sense Word Embeddings
Dai Quoc Nguyen
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Dat Quoc Nguyen
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Ashutosh Modi
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Stefan Thater
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Manfred Pinkal
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Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers
Sunghwan Mac Kim
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Steve Cassidy
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Improving Topic Coherence with Latent Feature Word Representations in MAP Estimation for Topic Modeling
Dat Quoc Nguyen
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Kairit Sirts
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Mark Johnson
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https://code.google.com/p/word2vec/
http://www-nlp.stanford.edu/projects/glove/
http://web.ist.utl.pt/acardoso/datasets/
http://www.sananalytics.com/lab/index.php
Field Of Study
Task
Language Understanding
Approach
Topic Modeling
Deep Learning
Generative Model
Language
English
Japanese
Dataset
News
Social Media
Twitter
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