POS Tagging Using Relaxation Labelling

Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies -to the maximum possible degree- a set of given constraints. This paper describes some experiments performed applying it to POS tagging, and the results obtained. It also ponders the possibility of applying it to word sense disambiguation.Comment: compressed & uuencoded postscript file. Paper length: 39 page

A Hybrid Environment for Syntax-Semantic Tagging

The thesis describes the application of the relaxation labelling algorithm to NLP disambiguation. Language is modelled through context constraint inspired on Constraint Grammars. The constraints enable the use of a real value statind "compatibility". The technique is applied to POS tagging, Shallow Parsing and Word Sense Disambigation. Experiments and results are reported. The proposed approach enables the use of multi-feature constraint models, the simultaneous resolution of several NL disambiguation tasks, and the collaboration of linguistic and statistical models.Comment: PhD Thesis. 120 page

Learning a Perceptron-Based Named Entity Chunker via Online Recognition Feedback

this paper we work with polynomial kernels K(x, x # ) = (x x # + 1) d , where d is the degree of the kerne

Named Entity Recognition for Catalan Using Spanish Resources

This work studies Named Entity Recognition (NER) for Catalan without making use of annotated resources of this language. The approach presented is based on machine learning techniques and exploits Spanish resources, either by first training models for Spanish and then translating them into Catalan, or by directly training bilingual models. The resulting models are retrained on unlabelied Catalan data using bootstrapping techniques. Exhaustive experimentation has been conducted on real data, showing competitive results for the obtained NER systems

An automata based approach to biomedical named entity recognition

ing an automata learning algorithm: Causal-State Splitting Reconstruction [1]. This algorithm has previously been applied to Named Entity Recognition [2] obtaining good results given the simplicity of the approach. The same approach has been applied to Biomedical NE identification, using GENIA corpus 3.0, with 10-fold cross-validation. Our system attained F1 = 73.14%. These results can be compared directly to [3] and [4], which used the same data. First system obtains F1 = 57.4% using ME Models, and the second one reports F1 = 79.2% using SVMs. Both improve their results using post-processing techniques, reaching F1 = 76.9% and F1 = 79.9% respectively. Our system does not use any post-processing techniques, and takes into acount few features, so the results are considered very promising. In future work some post-processing will be developed to improve the results

Proposals on Mapping Multilingual Hierarchies.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. We present a new approach for linking already existing lexical/semantic hierarchies. The Relaxation labeling algorithm is used to select -- among all the candidate connections proposed by a bilingual dictionary -- the right connection for each node in the taxonomy. We also propose several ways in which this technique could be applied to enrich and improve existing lexical databases

Experiments on Applying Relaxation Labeling to Map Multilingual Hierarchies.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. This paper presents a new approach for linking already existing hierarchies. The Relaxation labeling algorithm is used to select --among all the candidate connections proposed by a bilingual dictionary-- the right conection for each node in the taxonomy