3,244 research outputs found
Unsupervised Extraction of Representative Concepts from Scientific Literature
This paper studies the automated categorization and extraction of scientific
concepts from titles of scientific articles, in order to gain a deeper
understanding of their key contributions and facilitate the construction of a
generic academic knowledgebase. Towards this goal, we propose an unsupervised,
domain-independent, and scalable two-phase algorithm to type and extract key
concept mentions into aspects of interest (e.g., Techniques, Applications,
etc.). In the first phase of our algorithm we propose PhraseType, a
probabilistic generative model which exploits textual features and limited POS
tags to broadly segment text snippets into aspect-typed phrases. We extend this
model to simultaneously learn aspect-specific features and identify academic
domains in multi-domain corpora, since the two tasks mutually enhance each
other. In the second phase, we propose an approach based on adaptor grammars to
extract fine grained concept mentions from the aspect-typed phrases without the
need for any external resources or human effort, in a purely data-driven
manner. We apply our technique to study literature from diverse scientific
domains and show significant gains over state-of-the-art concept extraction
techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In other cases, the content of the topic itself may be of
intrinsic scientific interest.
Unfortunately, even using modern sparse techniques, the discovered topics are
often difficult to interpret due to the high dimensionality of the underlying
space. To improve topic interpretability, we introduce Graph-Sparse LDA, a
hierarchical topic model that leverages knowledge of relationships between
words (e.g., as encoded by an ontology). In our model, topics are summarized by
a few latent concept-words from the underlying graph that explain the observed
words. Graph-Sparse LDA recovers sparse, interpretable summaries on two
real-world biomedical datasets while matching state-of-the-art prediction
performance
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