We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polarities (positive and negative) of sentences within each product review before performing the actual machine learning task. While sentences with inconsistent polarities are removed, ...

In evaluating the output of language technology applications—MT, natural language generation, summarisation—automatic evaluation techniques generally conflate measurement of faithfulness to source content with fluency of the resulting text. In this paper we develop an automatic evaluation metric to estimate fluency alone, by examining the use of parser outputs as metrics, and show that they cor...

The paper presents work on improved sentence-level dialect classification of Egyptian Arabic (ARZ) vs. Modern Standard Arabic (MSA). Our approach is based on binary feature functions that can be implemented with a minimal amount of task-specific knowledge. We train a featurerich linear classifier based on a linear support-vector machine (linear SVM) approach. Our best system achieves an accurac...

SENTENCE LEVEL INFORMATION PATTERNS FOR NOVELTY DETECTION JULY 2006 XIAOYAN LI, B.E. TSINGHUA UNIVERSITY M.E., TSINGHUA UNIVERSITY Ph.D. UNIVERSITY OF MASSACHUSETTS AT AMHERST Directed by: Professor W. Bruce Croft The detection of new information in a document stream is an important component of many potential applications. In this thesis, a new novelty detection approach based on the identific...

This paper introduces a supervised approach for performing sentence level dialect identification between Modern Standard Arabic and Egyptian Dialectal Arabic. We use token level labels to derive sentence-level features. These features are then used with other core and meta features to train a generative classifier that predicts the correct label for each sentence in the given input text. The sy...

We describe the cursive script recognition module of a cheque processing system currently under development at the Centre for Pattern Recognition and Machine Intelligence (CENPARMI). Common systems perform recognition either on a character by character basis, or on a word level. The present study investigates the recognition at a higher level of abstraction, at the sentence level. Our computati...

I have constructed a sentence-level nonsense detector, with the goal of discriminating well-formed English sentences from the large volume of fragments, headlines, incoherent drivel, and meaningless snippets present in internet text. For many NLP tasks, the availability of large volumes of internet text is enormously helpful in combating the sparsity problem inherent in modeling language. Howev...