Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, using classifiers that team to distinguish human reference translations from machine translations.

We present a noun phrase coreference system that extends the work of Soon et al. (2001) and, to our knowledge, produces the best results to date on the MUC6 and MUC-7 coreference resolution data sets --- F-measures of 70.4 and 63.4, respectively.

that employs and extends current machine-learning techniques to semi-automatically find such mappings. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. Each learner exploits a different type

The main issue in e-learning is student modelling, i.e. the analysis of a student's behaviour and prediction of his/her future behaviour and learning performance. Indeed, it is difficult to monitor the students' learning behaviours. A solution

o M rice nate f e the i mode adopt ls due Whit vided ds; N ing. ML addresses the development of models that can learn from curacy of the constructed ML models to compute bed-load trans-experience and data Mitchell 1997. This has opened up new port is compared with the of some well-known bed

Motivation: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. Results: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern,

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