474 citations · 523 across the 5 of their papers we have counts for
5 papers
A Grounded Approach to Modeling Generic Knowledge Acquisition
Deniz Beser, Joe Cecil, Marjorie Freedman +4
We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an importan…
ADAM: A Sandbox for Implementing Language Learning
Ryan Gabbard, Deniz Beser, Jacob Lichtefeld +5
We present ADAM, a software system for designing and running child language learning experiments in Python. The system uses a virtual world to simulate a grounded language acquisit…
Text Chunking using Transformation-Based Learning
Lance A. Ramshaw, Mitchell P. Marcus
Eric Brill introduced transformation-based learning and showed that it can do part-of-speech tagging with fairly high accuracy. The same method can be applied at a higher level of…
Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
Lance A. Ramshaw, Mitchell P. Marcus
Eric Brill has recently proposed a simple and powerful corpus-based language modeling approach that can be applied to various tasks including part-of-speech tagging and building ph…
Pearl: A Probabilistic Chart Parser
David M. Magerman, Mitchell P. Marcus
This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the "best" parse of a sentence. The parser…