This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5% and 1...

Clinical observations and the results of many experimental researches indicate that individuals suffering from schizophrenia reveal difficulties in the recognition of emotional states experienced by other people; however the causes and the range of these problems have not been clearly described. Despite early research results confirming that difficulties in emotion recognition are related only ...

This paper explores techniques for utilizing untranscribed training data pools to increase the available training data for automatic speech recognition systems. It has been well established that current speech recognition technology, especially in Large Vocabulary Conversational Speech Recognition (LVCSR), is largely language independent, and that the dominant factor with regards to performance...

OBJECTIVE Schizophrenia patients often exhibit impairments in facial affect recognition which contribute to their poor social functioning. These impairments are stable in the course of the disorder and seem not to be affected by conventional treatment. The present study investigates the efficacy and specificity of a new training program for the remediation of such impairments. METHOD A newly ...

The paper discusses the usage of unlabeled data for Spanish Named Entity Recognition. Two techniques have been used: selftraining for detecting the entities in the text and co-training for classifying these already detected entities. We introduce a new co-training algorithm, which applies voting techniques in order to decide which unlabeled example should be added into the training set at each ...