MindNet is a knowledge representation project that uses our broad-coverage parser to build semantic networks from dictionaries, encyclopedias, and free text.
MindNet is a knowledge representation project that uses our broad-coverage parser to build semantic networks from dictionaries, encyclopedias, and free text. MindNets are produced by a fully automatic process that takes the input text, sentence-breaks it, parses each sentence to build a semantic dependency graph (Logical Form), aggregates these individual graphs into a single large graph, and then assigns probabilistic weights to subgraphs based on their frequency in the corpus as a whole. The project also encompasses a number of mechanisms for searching, sorting, and measuring the similarity of paths in a MindNet. We believe that automatic procedures such as MindNets provide the only credible prospect for acquiring world knowledge on the scale needed to support common-sense reasoning.
MindNet Browsing Now Available! If you are interested in more detailed information about MindNets, a small number of sample MindNets have been made available for online browsing at the mnex project homepage.
- Lucy Vanderwende, Gary Kacmarcik, Hisami Suzuki, and Arul Menezes, MindNet: an automatically-created lexical resource, in HLT/EMNLP Interactive Demonstrations Proceedings, October 2005.
- 鈴木久美, Gary Kacmarcik, Lucy Vanderwende, and Arul Menezes, Mindnet/mnex: Tools for automatic construction and analysis of semantic relations database (意味関係データベースの自動構築と解析のためのツール), in 言語処理学会第11回全国大会論文集, March 2005.
- Lucy Vanderwende, The Analysis of Noun Sequences using Semantic Information Extracted from On-Line Dictionaries, PhD thesis, Georgetown University, no. MSR-TR-95-57, October 1996.
- William Dolan, Stephen D. Richardson, and Lucy Vanderwende, Automatically Deriving Structured Knowledge Bases From On-Line Dictionaries, no. MSR-TR-93-07, May 1993.
- Simonetta Montemagni and Lucy Vanderwende, Structural Patterns vs. String Patterns for Extracting Semantic Information from Dictionaries, in Proceedings of the Fourteenth International Conference on Computational Linguistics, Association for Computational Linguistics, 1992.