Yan Qu


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Exploring Semantic Constraints for Document Retrieval
Hua Cheng | Yan Qu | Jesse Montgomery | David A. Evans
Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval?


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The Use of Monolingual Context Vectors for Missing Translations in Cross-Language Information Retrieval
Yan Qu | Gregory Grefenstette | David A. Evans
Second International Joint Conference on Natural Language Processing: Full Papers


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Finding Ideographic Representations of Japanese Names Written in Latin Script via Language Identification and Corpus Validation
Yan Qu | Gregory Grefenstette
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)


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Expanding lexicons by inducing paradigms and validating attested forms
Gregory Grefenstette | Yan Qu | David A. Evans
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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A Constraint-Based Approach for Cooperative Information-Seeking Dialogue
Yan Qu | Nancy Green
Proceedings of the International Natural Language Generation Conference


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Using Discourse Predictions for Ambiguity Resolution
Yan Qu | Carolyn P. Rose | Barbara Di Eugenio
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics


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Using Context in Machine Translation of Spoken Language
Lori Levin | Oren Glickman | Yan Qu | Carolyn P. Rose | Donna Gates | Alon Lavie | Alex Waibel | Carol Van Ess-Dykema
Proceedings of the Sixth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

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An Abstract Machine for Attribute-Value Logics
Bob Carpenter | Yan Qu
Proceedings of the Fourth International Workshop on Parsing Technologies

A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the amount of storage required when compared to interpreters or indirect compilers. In this paper, we describe the fundamental data structures and compilation techniques that we have employed to develop a unification and constraint-resolution engine capable of performance rivaling that of directly compiled Prolog terms while greatly exceeding Prolog in flexibility, expressiveness and modularity. In this paper, we will discuss the core architecture of our machine. We begin with a survey of the data structures supporting the small set of attribute-value logic instructions. These instructions manipulate feature structures by means of features, equality, and typing, and manipulate the program state by search and sequencing operations. We further show how these core operations can be integrated with a broad range of standard parsing techniques. Feature structures improve upon Prolog terms by allowing data to be organized by feature rather than by position. This encourages modular program development through the use of sparse structural descriptions which can be logically conjoined into larger units and directly executed. Standard linguistic representations, even of relatively simple local syntactic and semantic structures, typically run to hundreds of substructures. The type discipline we impose organizes information in an object-oriented manner by the multiple inheritance of classes and their associated features and type value constraints. In practice, this allows the construction of large-scale grammars in a relatively short period of time. At run-time, eager copying and structure-sharing is replaced with lazy, incremental, and localized branch and write operations. In order to allow for applications with parallel search, incremental backtracking can be localized to disjunctive choice points within the description of a single structure, thus supporting the kind of conditional mutual consistency checks used in modern grammatical theories such as HPSG, GB, and LFG. Further attention is paid to the byte-coding of instructions and their efficient indexing and subsequent retrieval, all of which is keyed on type information.