46,428 research outputs found
Ordering-sensitive and Semantic-aware Topic Modeling
Topic modeling of textual corpora is an important and challenging problem. In
most previous work, the "bag-of-words" assumption is usually made which ignores
the ordering of words. This assumption simplifies the computation, but it
unrealistically loses the ordering information and the semantic of words in the
context. In this paper, we present a Gaussian Mixture Neural Topic Model
(GMNTM) which incorporates both the ordering of words and the semantic meaning
of sentences into topic modeling. Specifically, we represent each topic as a
cluster of multi-dimensional vectors and embed the corpus into a collection of
vectors generated by the Gaussian mixture model. Each word is affected not only
by its topic, but also by the embedding vector of its surrounding words and the
context. The Gaussian mixture components and the topic of documents, sentences
and words can be learnt jointly. Extensive experiments show that our model can
learn better topics and more accurate word distributions for each topic.
Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM
obtains significantly better performance in terms of perplexity, retrieval
accuracy and classification accuracy.Comment: To appear in proceedings of AAAI 201
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
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