A Computational Study of Learning Functions in Statistical Language Models


A Computational Study of Learning Functions in Statistical Language Models – The problem of inference over the sequence of words was tackled in the last decade, and it is now recognized to be a fundamental problem of natural language processing. One important contribution of this work is a novel approach to data mining to discover the underlying underlying rules of natural language. It is a novel approach to the task of learning natural language from data.

Concordance detection on a large-scale data sets (i.e., large-scale text) is an important task. In this paper, we propose a novel method for concordance detection in text on large-scale text. We show that using multiple annotated texts and annotated examples to infer consensus results is computationally faster. However, the proposed method significantly exceeds the performance of existing work on concordance detection on a large-scale text dataset. To avoid the need to annotate large-scale text for prediction, and more importantly, avoid high-level annotations, we devise an efficient algorithm which simultaneously infer consensus results and annotate the entire text. We evaluate the proposed approach by performing extensive experiments on several large-scale data sets. In particular, we demonstrate the superior performance in terms of accurate identification of consensus results by using only annotated examples and annotated examples to construct the consensus trees.

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A Computational Study of Learning Functions in Statistical Language Models

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  • Unsupervised Unsupervised Learning Based on Low-rank Decomposition of Semantically-intact Data

    Learning to Communicate with Unusual Object DescriptionsConcordance detection on a large-scale data sets (i.e., large-scale text) is an important task. In this paper, we propose a novel method for concordance detection in text on large-scale text. We show that using multiple annotated texts and annotated examples to infer consensus results is computationally faster. However, the proposed method significantly exceeds the performance of existing work on concordance detection on a large-scale text dataset. To avoid the need to annotate large-scale text for prediction, and more importantly, avoid high-level annotations, we devise an efficient algorithm which simultaneously infer consensus results and annotate the entire text. We evaluate the proposed approach by performing extensive experiments on several large-scale data sets. In particular, we demonstrate the superior performance in terms of accurate identification of consensus results by using only annotated examples and annotated examples to construct the consensus trees.


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