Information Consolidation

Motivation

Although existing search engines are effective in identifying relevant documents among bilions on non-relevant ones, they remain weak at isolating the facts of users' interest within these documents, let alone organizing and presenting this knowledge intuitively and concisely. Searchers have to laborously skim through all retrieved documents and collect the statements that are relevant to their information needs.

For example, a public decision maker in the domain of education wants to learn positive and negative experiences with a particular policy across countries, its impact on various populations, etc. So far, such information must have been consolidated by field experts, which is costly and time-consuming.

Goals

This project targets the big next step in information access technology by

• Automatically identifying relevant statements
• Consolidating the information and inferring relations between the statements
• Enabling users to explore the consolidated information

Methods

The progress of the project will be led by an iterative methodology that encompasses the following:

• Corpus – large data set of partially annotated data in the domain of educational topics acquired using focused crawling and web-based annotation tools
• Linguistic annotation on various levels (syntax, semanantic roles, word senses, named entities, co-reference resolution, truth values, and other domain-specific ones) using state-of-the-art automatic annotation methods
• Extracting atomic statements – by adapting and extending open information extraction techniques
• Reflecting relationships between statements – by applying textual entailment and semantic similarity methods
• Knowledge exploration – effective and efficient user interfaces for interactively displaying statements relevant to user queries

Results

In the project, the following corpora were created:

• DIP 2016 Corpus
• Domain-specific context-sensitive semantic verb relations

Team

• Prof. Dr. Iryna Gurevych, Principal Investigator
• Dr. Ing. Nils Reimers
• M.Sc. Michael Bugert, Doctoral Researcher
• M.Sc. Yevgeniy Puzikov, Doctoral Researcher
• M.Sc. Max Glockner, Doctoral Researcher

Former staff:

• Dr. Eugenio Martínez Cámara, Postdoctoral Researcher
• Dr. Judith Eckle-Kohler, Senior Researcher
• Dr. Ivan Habernal
• M.Sc. Maria Sukhareva

Partners

Department of Compute Science, Bar-Ilan University, Israel

• Prof. Dr. Ido Dagan

Department of Information Science, Bar-Ilan University, Israel

• Prof. Dr. Judit Bar-Ilan

Faculty of Industrial Engineering and Management, Technion – Israel Institute of Technology

• Prof. Dr. Oren Kurland

Teaching

Teaching activities of the current team of the project:

• SS 2016: Lexical-Semantic Methods for Language Understanding
• WS 2015/2016: Understanding Deep Learning for Natural Language Understanding

Student theses

Master theses:

• Can Diehl. Automatic Aggregation of Argument Components. 2016 Supervised by: Dr. Christian Stab and Prof. Iryna Gurevych

Bachelor theses:

• Michelle Peters. Broad-coverage distantly supervised verb sense disambiguation. 2016 Supervised by: Dr. Judith Eckle-Kohler and Prof. Iryna Gurevych

Funding

This project is funded by:

• Funder: Deutsche Forschungsgemeinschaft (German Research Foundation)
• Programme: DIP Programme; 17. Round of the German-Israeli project co-operation
• Grant code: GU 798/17-1 and DA 1600/1-1
• More information: funder web page of the project