30 citations · 72 across the 5 of their papers we have counts for
5 papers
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
Fei Tian, Bin Gao, Di He +1
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence an…
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Fei Tian, Bin Gao, Enhong Chen +1
Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may…
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Huazheng Wang, Fei Tian, Bin Gao +2
Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which…
WordRep: A Benchmark for Research on Learning Word Representations
Bin Gao, Jiang Bian, Tie-Yan Liu
WordRep is a benchmark collection for the research on learning distributed word representations (or word embeddings), released by Microsoft Research. In this paper, we describe the…
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
Qing Cui, Bin Gao, Jiang Bian +2
Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining, information retrieval, and…