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Choh Man Teng

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3 papers

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AAAI Conference 2018 Conference Paper

Effective Broad-Coverage Deep Parsing

James Allen
Omid Bahkshandeh
William de Beaumont
Lucian Galescu
Choh Man Teng

Current semantic parsers either compute shallow representations over a wide range of input, or deeper representations in very limited domains. We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar. This paper discusses how this core system can be customized for a particularly challenging domain, namely reading research papers in biology. We evaluate these customizations with some ablation experiments.

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AAAI Conference 2017 System Paper

Natural Language Dialogue for Building and Learning Models and Structures

Ian Perera
James Allen
Lucian Galescu
Choh Man Teng
Mark Burstein
Scott Friedman
David McDonald
Jeffrey Rye

We demonstrate an integrated system for building and learning models and structures in both a real and virtual environment. The system combines natural language understanding, planning, and methods for composition of basic concepts into more complicated concepts. The user and the system interact via natural language to jointly plan and execute tasks involving building structures, with clarifications and demonstrations to teach the system along the way. We use the same architecture for building and simulating models of biology, demonstrating the general-purpose nature of the system where domain-specific knowledge is concentrated in sub-modules with the basic interaction remaining domain-independent. These capabilities are supported by our work on semantic parsing, which generates knowledge structures to be grounded in a physical representation, and composed with existing knowledge to create a dynamic plan for completing goals. Prior work on learning from natural language demonstrations enables learning of models from very few demonstrations, and features are extracted from definitions in natural language. We believe this architecture for interaction opens up a wide possibility of human-computer interaction and knowledge transfer through natural language.

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IS Journal 2004 Journal Article

Polishing blemishes: issues in data correction

Choh Man Teng

Data quality is crucial to any data analysis task. Many imperfection-handling techniques avoid overfitting or simply remove offending portions of the data. Polishing identifies blemishes in the data and makes corrections to retain and recover as much information as possible. When using information collected from channels susceptible to disturbances, data quality is a concern-especially when the primary objective is to assimilate and understand the data. Imperfections can arise from many sources, including transmission and bandwidth constraints, faults in sensor devices, irregularities in sampling, and transcription errors. An intuitive application that exemplifies handling data imperfections is the spell-checker. Developing such a spell-checker would require novel techniques for repairing data imperfections. We are exploring such techniques using a data correction method called polishing. Here, we compare polishing to two alternative approaches to handling data imperfections, focusing on how to evaluate and validate data correction mechanisms.

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