Topic Similarity Networks: Visual Analytics for Large Document Sets

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData 2014

E3 : Keyphrase based News Event Exploration Engine

This paper presents a novel system E3 for extracting keyphrases from news content for the purpose of offering the news audience a broad overview of news events, with especially high content volume. Given an input query, E3 extracts keyphrases and enrich them by tagging, ranking and finding role for frequently associated keyphrases. Also, E3 finds the novelty and activeness of keyphrases using news publication date, to identify the most interesting and informative keyphrases

Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

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

Automatic Taxonomy Generation - A Use-Case in the Legal Domain

A key challenge in the legal domain is the adaptation and representation of the legal knowledge expressed through texts, in order for legal practitioners and researchers to access this information easier and faster to help with compliance related issues. One way to approach this goal is in the form of a taxonomy of legal concepts. While this task usually requires a manual construction of terms and their relations by domain experts, this paper describes a methodology to automatically generate a taxonomy of legal noun concepts. We apply and compare two approaches on a corpus consisting of statutory instruments for UK, Wales, Scotland and Northern Ireland laws.Comment: 9 page