Parsing Constituents With

A parser based on Categorial Grammars is described. It is capable of parsing constituents independently, and employs a chart without top-down control. It can be used to impose contraints on the input, or to enquire about the possible final compositions

Turkish Natural Language Processing Initiative: An Overview

: This paper presents an overview of a research project aimed at establishing a computational infrastructure for building NLP applications for Turkish. The project specification includes design and implementation of re-usable software tools and advanced NLP applications. 1 Introduction Turkish Natural Language Processing Initiative 1 (TNLP) is a collaborative research effort for computational analyses of Turkish text and construction of software tools for NLP applications in Turkish. Although Turkish has been quite popular in linguistics literature, there have been very few computational studies before the 1990s. One can possibly point to works such as Hankamer's ke¸ci system[5], Koksal's thesis [9], and Sagay[12] and Stoop's[13] translation systems. However, until recently no substantial work on syntactic and semantic processing of Turkish had been taken, despite the obvious need for computational processing of Turkish in many applications. Turkish is characterized by certain morph..

Memory-Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text

We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and cousol hypotheses. The learn-ing algorithms are based on applying causal heuristicsto precedents of new in-formation. The heuristics are derived from principles of causation, and, to a limited extent, from domain-related causal reasoning. learning is defined as finding--and later augmenting-inter-episodal and intro-episodal causal connec-tions. The learning algorithms enable inductive generalization df causal asso-ciations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS