Boris V. Dobrov

Socio-political thesaurus in concept-based information retrieval

In CLEF 2005 experiments we used a bilingual Russian-English Socio-Political Thesaurus that we developed over more than 10 years as a tool for automatic text processing in information retrieval tasks. The same resource and the same algorithms were used for the ad-hoc and domain–specific task.

Combining evidence for automatic extraction of terms

The paper describes the method of extraction of two-word domain terms combining their features. The features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statistics of global search engines, and a domain-specific thesaurus. The evaluation of the approach is based on the terminology of manually created thesauri. We show that the use of multiple features considerably improves the automatic extraction of domain-specific terms. We compare the quality of the proposed method in two different domains.

RuThes Thesaurus in Detecting Russian Paraphrases

In this paper we study the contribution of semantic features to the detection of Russian paraphrases. The features were calculated on the Russian Thesaurus RuThes. First, we applied RuThes synonyms in clustering news articles, many of which had been created with rewriting (that is paraphrasing) of source news, and found significant improvement. Second, we applied several semantic similarity measures proposed for English thesaurus WordNet to RuThes thesaurus and utilized them for detecting Russian paraphrased sentences.

Thesaurus-Based Topic Models and Their Evaluation

In this paper we study thesaurus-based topic models and evaluate them from the point of view of topic coherence. Thesaurus-based topic model enhances scores of related terms found in the same text, which means that the model encourages these terms to be in the same topics. We evaluate various variants of such models. At the first step, we carry out manual evaluation of the obtained topics. At the second step, we study the possibility to use the collected manual data for evaluating new variants of thesaurus-based models, propose a method and select the best of its parameters in cross-validation. At the third step, we apply the created evaluation method to estimate the influence of word frequencies on adding thesaurus relations during generating topic models.

News Timeline Generation: Accounting for Structural Aspects and Temporal Nature of News Stream

The number of news articles that are published daily is larger than any person can afford to study. Correct summarization of the information allows for an easy search for the event of interest. This research was designed to address the issue of constructing annotations of news story. Standard multi-document summarization approaches are not able to extract all information relevant to the event. This is due to the fact that such approaches do not take into account the variability of the event context in time. We have implemented a system that automatically builds timeline summary. We investigated impact of three factors: query extension, accounting for temporal nature and structure of news article in form of inverted pyramid. The annotations that we generate are composed of sentences sorted in chronological order, which together contain the main details of the news story. The paper shows that taking into account the described factors positively affects the quality of the annotations created.

On-line Thematic and Metadata Analysis of Document Collection

We describe interactive search tools based on thematic and metadata analysis of a document collection. The tools provide the following facilities: estimation of the main themes discussed in a document set, interactive narrowing of a query, cross-language search, exploring the topic-time dependencies, and exploring the dependencies between authors of articles, scientific organizations and themes. We describe an algorithm for thematic and metadata analysis and show some examples of their applications. All the tools described are integrated into the large full-text information retrieval system UIS RUSSIA.