The goal of the expert search task is finding knowledgeable persons within the enterprise. In this paper we focus on its distinctions from the other information retrieval tasks. We review the existing ap- proaches and propose a new term weighting scheme which is based on analysis of communication patterns between people. The effectiveness of the proposed approach is evaluated on a collection of e-mails from an organization of approximately 1500 people. Results show that it is possible to take into account communication structure in the process of term weighting, effectively combining communication-based and document-based approaches to expert finding.
The present paper deals with word sense induction from lexical co-occurrence graphs. We construct such graphs on large Russian corpora and then apply the data to cluster the results of Mail.ru search according to meanings in the query. We compare different methods of performing such clustering and different source corpora. Models of applying distributional semantics to big linguistic data are described.