The paper presents the use of a two structural model committee, where the output of the first model together with its confidence is set as the input of the second model. The confidence for the given context of predictions in the sequence is extracted from the alternative hypotheses generated from the first model. We present experiments on the shallow… (More)
Named entity (NE) recognition is a core technology for understanding low level semantics of texts. In this paper we report our preliminary results for Named Entity Recognition on MUC 7 corpus by combining the supervised machine learning system in the form of probabilistic generative Hidden Markov Model (HMM) for named entity classes PERSON, ORGANIZATION and… (More)
In this paper we present efficient implementation of an algorithm for sequential training of support vector machine. Algorithm is obtained by maintaining Karush-Kuhn-Tucker optimality conditions while learning from new example. We have tested the performance of our implementation on widely recognized classification benchmark tests.