Erika F. de Lima. Assigning Grammatical Relations with a Back-off Model. Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, 1997.
This paper presents a corpus-based method to assign grammatical subject/object relations to ambiguous German constructs. It makes use of an unsupervised learning procedure to collect training and test data, and the back-off model to make assignment decisions.
Atro Voutilainen, Lluis Padro. Developing a Hybrid NP Parser. Proceedings of the 5th ANLP, 1997.
We describe the use of energy function optimization in very shallow syntactic parsing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpus-based n-grams of syntactic tags. The success of the hybrid syntactic disambiguator is evaluated against a held-out benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated.
Michael Elhadad and Jacques Robin. Controlling Content Realization with Functional Unification Grammers. In Aspects of Automated Natural Language Generation. R. Dale, E. Hovy, D. Rosner and O. Stock editors. Springer Verlag, 1992.
S.J. Aaronson. Optimal Demand-Oriented Topology for Hypertext Systems. SIGIR 97.
Damien Genthial, Jacques Courtin, and Jacques Menezo. Towards a More User-friendly Correction. COLING'94.
We first present our view of detection and correction of syntactic errors. We then introduce a new correction method, based on heuristic criteria used to decide which correction should be preferred. Weighting of these criteria leads to a flexible and parametrable system, which can adapt itself to the user. A partitioning of the trees based on linguistic criteria: agreement rules, rather than computational criteria is then necessary. We end by proposing extensions to lexical correction and to some syntactic errors. Our aim is an adaptable and user-friendly system capable of automatic correction for some applications.
Andrew Kehler (SRI International). Probabilistic Coreference in Information Extraction. In Proceedings of the Second Conference on Empirical Methods in NLP (EMNLP-2), August 1-2, 1997.
Certain applications require that the output of an information extraction system be probabilistic, so that a downstream system can reliably fuse the output with possibly contradictory information from other sources. In this paper we consider the problem of assigning a probability distribution to alternative sets of coreference relationships among entity descriptions. We present the results of initial experiments with several approaches to estimating such distributions in an application using SRI's FASTUS information extraction system.
D. Beeferman, A. Berger, and J. Lafferty. Text Segmentation Using Exponential Models. Proceedings of the Second Conference On Empirical Methods in NLP, Providence, RI, 1997.
35th Annual Meeting of the Association for Computational Linguistics.
Second Conference On Empirical Methods in Natural Language Processing.