Benjamin Han

From Language to Time: A Temporal Expression Anchorer

Understanding temporal expressions in natural language is a key step towards incorporating temporal information in many applications. In this paper we describe a system capable of anchoring such expressions in English: system TEA features a constraint-based calendar model and a compact representational language to capture the intensional meaning of temporal expressions. We also report favorable results from experiments conducted on several email datasets

A framework for resolution of time in natural language

Automatic extraction and reasoning over temporal properties in natural language discourse has not had wide use in practical systems due to its demand for a rich and compositional, yet inference-friendly, representation of time. Motivated by our study of temporal expressions from the Penn Treebank corpora, we address the problem by proposing a two-level constraint-based framework for processing and reasoning over temporal information in natural language. Within this framework, temporal expressions are viewed as partial assignments to the variables of an underlying calendar constraint system, and multiple expressions together describe a temporal constraint-satisfaction problem (TCSP). To support this framework, we designed a typed formal language for encoding natural language expressions. The language can cope with phenomena such as under-specification and granularity change. The constraint problems can be solved using various constraint propagation and search methods, and the solutions can then be used to answer a wide range of time-related queries.

Deriving minimal conflict sets by CS-trees with mark set in diagnosis from first principles

To discriminate among all possible diagnoses using Hous theory of measurement in diagnosis from first principles, one has to derive all minimal conflict sets from a known conflict set. However, the result derived from Hous method depends on the order of node generation in CS-trees. We develop a derivation method with mark set to overcome this drawback of Hous method. We also show that our method is more efficient in the sense that no redundant tests have to be done. An enhancement to our method with the aid of extra information is presented. Finally, a discussion on top-down and bottom-up derivations is given.

A model for multimodal information retrieval

Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimodal information retrieval systems (MMIR). A general model for multimodal information retrieval is proposed by which a users information need is expressed through composite, multimodal queries, and the most appropriate weighted combination of indexing techniques is determined by a machine learning approach in order to best satisfy the information need. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.

Understanding Temporal Expressions in Emails

Recent years have seen increasing research on extracting and using temporal information in natural language applications. However most of the works found in the literature have focused on identifying and understanding temporal expressions in newswire texts. In this paper we report our work on anchoring temporal expressions in a novel genre, emails. The highly under-specified nature of these expressions fits well with our constraint-based representation of time, Time Calculus for Natural Language (TCNL). We have developed and evaluated a Temporal Expression Anchoror (TEA), and the result shows that it performs significantly better than the baseline, and compares favorably with some of the closely related work.

Domain portability in speech-to-speech translation

Speech-to-speech translation has made significant advances over the past decade, with several high-visibility projects (C-STAR, Verb-mobil, the Spoken Language Translator, and others) significantly advancing the state-of-the-art. While speech recognition can currently effectively deal with very large vocabularies and is fairly speaker independent, speech translation is currently still effective only in limited, albeit large, domains. The issue of domain portability is thus of significant importance, with several current research efforts designed to develop speech-translation systems that can be ported to new domains with significantly less time and effort than is currently possible.

A genetic algorithm approach to measurement prescription in fault diagnosis

Abstract To fully discriminate among all possible diagnoses in a fault diagnosis task, one needs to take measurements from the system being diagnosed. The primary effects of taking one measurement in diagnosis based on first principles were presented in A. Reiter [Artificial Intelligence (32) (1987) 57–95] and a more detailed, formal account was given in A. Hou [Artificial Intelligence (65) (1994) 281–328]. However, the order in which measurements are to be taken is an issue. We propose a genetic algorithm to determine a good measurement order for a diagnosis task. The method applies operators such as selection, crossover, and mutation to evolve an initial population of measurement sequences. The quality of a measurement sequence is evaluated based on the cost taken for the measurement sequence to find the final diagnosis. Experiments on testing circuits have shown that the quality of measurement sequences is greatly improved after evolution.

A Model-Based Diagnosis System for Identifying Faulty Components in Digital Circuits

We describe the ideas and implementation of a model-based diagnosis system for digital circuits. Our work is based on Reiters theory of diagnosis from first principles [14], incorporated with Hous theory of measurements [17], to derive possible diagnoses in a fault diagnosis task. To determine the best order in which measurements are to be taken, a measurement selection strategy using the genetic algorithm (MSSGA) is proposed. A circuit description language for describing circuits hierarchically is given. An efficient propositional logic prover used for consistency checking based on the trie structure is developed [22]. An example run is given to illustrate the working of the system. Finally, a comparison with other systems is discussed, and possible extensions to our system are described.

Comments on the theory of measurement in diagnosis from first principles

Abstract When finding diagnoses from first principles, one needs to refine possible diagnoses by making measurements from the system being diagnosed. Based on the work of Reiter, Hou has developed and formalized an efficient incremental method for computing all diagnoses upon measurement. However, we feel that some points in Hou’s paper need clarifications. In this paper, we describe an elaborate picture of the relationships among measurements, conflict sets and diagnoses. We also present some comments on the equivalence relation and Hou’s procedure for conflict recognition.

An improvement to Hou's approach of deriving minimal conflict sets

To discriminate among all possible diagnoses using Hous theory of measurement in diagnosis from first principles, one has to derive all minimal conflict sets from a known conflict set. We find that Hous method for the derivation fails to find all minimal conflict sets in some cases, hence it is incomplete. In this paper we develop a complete and efficient method for the derivation, in which no redundant tests ever occur. Furthermore, we discuss two more possible improvements to our method. Finally, we discuss the performance comparison between the top-down approach described in this paper and a bottom-up method for the derivation.