In this paper, we first address the measurements to improve classification accuracy for parametric trajectory modeling (PTM), exploring the effect of context -dependent information, prosody knowledge (pitch, duration) and derivative features (to depict speech dynamics further besides the advantage of PTM on this aspect). Experiment shows 61.585% error reduction with these techniques. We then us...

Recognition errors hinder the proliferation of speech recognition (SR) systems. Based on the observation that recognition errors may result in ungrammatical sentences, especially in dictation application where an acceptable level of accuracy of generated documents is indispensable, we propose to incorporate two kinds of linguistic features into error detection: lexical features of words, and sy...

This paper describes an English grammatical error correction system for CoNLL2013 shared task. Error types covered by our system are article/determiner, preposition, and noun number agreement. This work is our first attempt on grammatical error correction research. In this work, we only focus on reimplementing the techniques presented before and optimizing the performance. As a result of the im...

background and objectives: patient safety (ps) is one of the most important and essential elements of quality in healthcare setting. a systematic review and meta-analysis was performed to assess the status of patient safety culture using the hospital survey on patient safety culture (hsopsc). methods: in this systematic review and meta-analysis study, data were collected through searching datab...

State-of-art systems for grammar error correction often correct errors based on word sequences or phrases. In this paper, we describe a grammar error correction system which corrects grammatical errors at tree level directly. We cluster all error into two groups and divide our system into two modules correspondingly: the general module and the special module. In the general module, we propose a...

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 this paper we present the Arib system for Arabic spelling error detection and correction as part of the second Shared Task on Automatic Arabic Error Correction. Our system contains many components that address various types of spelling error and applies a combination of approaches including rule based, statistical based, and lexicon based in a cascade fashion. We also employed two core model...

This paper describes our system in the shared task of CoNLL-2013. We illustrate that grammatical error detection and correction can be transformed into a multiclass classification task and implemented as a single-model system regardless of various error types with the aid of maximum entropy modeling. Our system achieves the F1 score of 17.13% on the standard test set.

Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous out...