Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based ...

A new word-matching method is proposed that is able to achieve matching speedily and correctly even in the presence of noise at the beginning, end or within the word. This method can use the discriminant function in order to choose the most fitting word from the database. These functions take into consideration factors such as the character recognition rate and word segmentation rate. In additi...

Word level training refers to the process of learning the parameters of a word recognition system based on word level criteria functions. Previously, researchers trained lexicon-driven handwritten word recognition systems at the character level individually. These systems generally use statistical or neural based character recognizers to produce character level confidence scores. In the case of...

Word sense disambiguation (WSD) is an intermediate task within information retrieval and information extraction, attempting to select the proper sense of ambiguous words. Due to the scarcity of training data, semi-supervised learning, which profits from seed annotated examples and a large set of unlabeled data, are worth researching. We present preliminary results of two semi-supervised learnin...

We present an empirical comparison of major classification algorithms when training data contains attribute noise levels not representative of field data. Although conventional wisdom indicates that training data should contain noise representative of field data, it can be difficult to ensure representative noise levels. To study classification algorithm sensitivity, we develop an innovative ex...

During language acquisition, infants frequently encounter ambient noise. We present a computational model to address whether specific acoustic processing abilities are necessary to detect known words in moderate noise--an ability attested experimentally in infants. The model implements a general purpose speech encoding and word detection procedure. Importantly, the model contains no dedicated p...

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot...

It is essential to ensure a satisfactory QoS (Quality of Service) when offering a speech communication system with a noise reduction algorithm. In this paper, we propose a new obejective test methodology for noise-reduced speech that estimates word intelligibility by using a distortion measure. Experimental results confirmed that the proposed methodology gives an accurate estimate with independ...