Scaling question answering to the web
ACM Transactions on Information Systems (TOIS)
Mining the web for answers to natural language questions
Proceedings of the tenth international conference on Information and knowledge management
Mining the web to create minority language corpora
Proceedings of the tenth international conference on Information and knowledge management
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Introduction to the special issue on the web as corpus
Computational Linguistics - Special issue on web as corpus
Is it the right answer?: exploiting web redundancy for Answer Validation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Frequency estimates for statistical word similarity measures
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Two web-based approaches for noun sense disambiguation
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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A user expresses her information need through words with a precise meaning, but from the machine point of view this meaning does not come with the word. A further step is needful to automatically associate it to the words. Techniques that process human language are required and also linguistic and semantic knowledge, stored within distinct and heterogeneous resources, which play an important role during all Natural Language Processing (NLP) steps. Resources management is a challenging problem, together with the correct association between URIs coming from the resources and meanings of the words.This work presents a service that, given a lexeme (an abstract unit of morphological analysis in linguistics, which roughly corresponds to a set of words that are different forms of the same word), returns all syntactic and semantic information collected from a list of lexical and semantic resources. The proposed strategy consists in merging data with origin from stable resources, such as WordNet, with data collected dynamically from evolving sources, such as the Web or Wikipedia. That strategy is implemented in a wrapper to a set of popular linguistic resources that provides a single point of access to them, in a transparent way to the user, to accomplish the computational linguistic problem of getting a rich set of linguistic and semantic annotations in a compact way.