Hefbib : hierarchical expert finding in heterogeneous bibliographic network

Expert finding systems allow users to type simple text queries and retrieve names of individuals who possess the expertise described in the queries. Such applications are especially useful in real world: conference orga- nizers may search for reviewers, company recruiters may search for talented candidates, graduate students may search for advisers and researchers may search for collaborators, etc. In this study, we propose Hefbib, a hierarchical approach to expert finding in heterogeneous bibliographic network, to construct an expert hierarchy given a seed textual topic hierarchy as well as retrieve authoritative experts given a search query. Experiments on synthetic toy examples and real-world DBLP dataset show promising results

Hierarchical viewpoint discovery from tweets using Bayesian modelling

When users express their stances towards a topic in social media, they might elaborate their viewpoints or reasoning. Oftentimes, viewpoints expressed by different users exhibit a hierarchical structure. Therefore, detecting this kind of hierarchical viewpoints offers a better insight to understand the public opinion. In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed to follow two nested Chinese restaurant processes. Moreover, opinions in text are often expressed in un-semantically decomposable multi-terms or phrases, such as ‘economic recession’. Hence, a hierarchical Pitman–Yor process is employed as a prior for modelling the generation of phrases with arbitrary length. Experimental results on two Twitter corpora demonstrate the effectiveness of the proposed Bayesian model for hierarchical viewpoint discovery

Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling

In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, hrLDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of hrLDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from hrLDA are very competitive with the ontologies created by domain experts

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

Researcher profiling: Finding representative phrases for researchers

We are working on building a comprehensive search system for a researcher given his/her name and affiliation. The output result includes the researcher’s basic profile, his/her research publications, past grants received, patents, and Youtube or any other video links. In this paper, we utilize an existing framework and propose a method to accurately generate meaningful and representative phrases for one researcher, based on his/her publication titles from the search results of the aforementioned system. The purpose of the research is to provide a thorough understanding of the researcher’s interest based on limited input. Although the algorithm requires some background context given the limited size of input, the quality of the phrases generated is satisfactory. We also discuss our approach to generate personalized phrase representation for two or more researchers working in a similar field.Ope