Joshi, Aravind K
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PublicationSome Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars(1995-05-01) Joshi, Aravind K; Joshi, Aravind KIn this paper we present some novel applications of Explanation-Based Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The novel aspects are (a) immediate generalization of parses in the training set, (b) generalization over recursive structures and (c) representation of generalized parses as Finite State Transducers. A highly impoverished parser called a “stapler” has also been introduced. We present experimental results using EBL for different corpora and architectures to show the effectiveness of our approach. PublicationACUMEN: Amplifying Control and Understanding of Multiple ENtities(2002-07-01) Allbeck, Jan; Kipper, Karin; Adams, Charles; Badler, Norman I; Zoubanova, Elena; Joshi, Aravind K; Palmer, Martha; Joshi, Aravind KIn virtual environments, the control of numerous entities in multiple dimensions can be difficult and tedious. In this paper, we present a system for synthesizing and recognizing aggregate movements in a virtual environment with a high-level (natural language) interface. The principal com- ponents include: an interactive interface for aggregate con- trol based on a collection of parameters extending an exist- ing movement quality model, a feature analysis of aggregate motion verbs, recognizers to detect occurrences of features in a collection of simulated entities, and a clustering algorithm that determines subgroups. Results based on simulations and a sample instruction application are shown. PublicationDisambiguation of Super Parts of Speech (or Supertags): Almost Parsing(1995-04-01) Joshi, Aravind K.; Joshi, Aravind K.; Srinivas, B.In a lexicalized grammar formalism such as Lexicalized Tree-Adjoining Grammar (LTAG), each lexical item is associated with at least one elementary structure (supertag) that localizes syntactic and semantic dependencies. Thus a parser for a lexicalized grammar must search a large set of supertags to choose the right ones to combine for the parse of the sentence. We present techniques for disambiguating supertags using local information such as lexical preference and local lexical dependencies. The similarity between LTAG and Dependency grammars is exploited in the dependency model of supertag disambiguation. The performance results for various models of supertag disambiguation such as unigram, trigram and dependency-based models are presented.