Expert finding has become an important retrieval task. Expert finding is about finding people rather than documents and the goal is to retrieve a ranked list of candidates/experts with expertise on a given topic. In this paper, we describe an expertfinding system that reasons about the relevance of a candidate to a given expertise area. The system utilizes plausible inferences to infer the rele...

Automatic processing of text documents requires techniques that can go beyond the lexical level, and are able to handle the semantics underlying natural language sentences. A support for such techniques can be provided by taxonomies that connect terms to the underlying concepts, and concepts to each other according to different kinds of relationships. An outstanding example of such a kind of re...

We report on the use of medical facts to support the enhancement of natural language processing of biomedical text. Inferencing in semantic interpretation depends on a fact repository as well as an ontology. We used statistical methods to construct a repository of drug-disorder co-occurrences from a large collection of clinical notes, and this resource is used to validate inferences automatical...

Text data has recently been used as evidence in estimating the political ideologies of individuals, including political elites and social media users. While inferences about people are often the intrinsic quantity of interest, we draw inspiration from open information extraction to identify a new task: inferring the political import of propositions like OBAMA IS A SOCIALIST. We present several ...

This paper presents PLIS, an open source Probabilistic Lexical Inference System which combines two functionalities: (i) a tool for integrating lexical inference knowledge from diverse resources, and (ii) a framework for scoring textual inferences based on the integrated knowledge. We provide PLIS with two probabilistic implementation of this framework. PLIS is available for download and develop...