Introduction to the special issue on the web as corpus
Computational Linguistics - Special issue on web as corpus
A compact architecture for dialogue management based on scripts and meta-outputs
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Error mining for wide-coverage grammar engineering
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Bootstrapping deep lexical resources: resources for courses
DeepLA '05 Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition
Using unknown word techniques to learn known words
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Acquisition of unknown word paradigms for large-scale grammars
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A machine learning approach to relational noun mining in German
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
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In this paper we illustrate and underline the importance of making detailed linguistic information a central part of the process of automatic acquisition of large-scale lexicons as a means for enhancing robustness and at the same time ensuring maintainability and re-usability of deep lexicalised grammars. Using the error mining techniques proposed in (van Noord, 2004) we show very convincingly that the main hindrance to portability of deep lexicalised grammars to domains other than the ones originally developed in, as well as to robustness of systems using such grammars is low lexical coverage. To this effect, we develop linguistically-driven methods that use detailed morphosyntactic information to automatically enhance the performance of deep lexicalised grammars maintaining at the same time their usually already achieved high linguistic quality.