TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Practical NLP-Based Text Indexing
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Extraction of complex index terms in non-English IR: A shallow parsing based approach
Information Processing and Management: an International Journal
Text Retrieval through Corrupted Queries
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Corrupted queries in Spanish text retrieval: error correction vs. N-Grams
Proceedings of the 2nd ACM workshop on Improving non english web searching
A reconfigurable stochastic tagger for languages with complex tag structure
MorphSlav '03 Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages
XML rules for enclitic segmentation
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Contextual spelling correction
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Managing misspelled queries in IR applications
Information Processing and Management: an International Journal
COLE experiments at QA@CLEF 2004 spanish monolingual track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
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Current taggers assume that input texts are already tokenized, i.e. correctly segmented in tokens or high level information units that identify each individual component of the texts. This working hypothesis is unrealistic, due to the heterogeneous nature of the application texts and their sources. The greatest troubles arise when this segmentation is ambiguous. The choice of the correct segmentation alternative depends on the context, which is precisely what taggers study.In this work, we develop a tagger able not only to decide the tag to be assigned to every token, but also to decide whether some of them form or not the same term, according to different segmentation alternatives. For this task, we design an extension of the Viterbi algorithm able to evaluate streams of tokens of different lengths over the same structure. We also compare its time and space complexities with those of the classic and iterative versions of the algorithm.