This paper describes the NMSU-Pitt-UNCA word-sense disambiguation system participating in the Senseval-3 English lexical sample task. The focus of the work is on using semantic class-based collocations to augment traditional word-based collocations. Three separate sources of word relatedness are used for these collocations: 1) WordNet hypernym relations; 2) cluster-based word similarity classes...

Text documents can be watermarked by patterning the inter-word spaces. This paper proposes a text watermarking algorithm that exploits the novel concepts of word classification and inter-word space statistics. The words are classified using some features. Several adjacent words are grouped into a segment, and the segments are also classified using the word class information. The same amount of ...

On the basis of neuropsychological and functional imaging evidence, meaning and grammatical class (particularly the verb-noun distinction) have been proposed as organizational principles of linguistic knowledge in the brain. However, previous studies investigating verb and noun processing have been confounded by the presence of systematic correlations between word meaning and grammatical class....

This paper describes an exponential family model of word sense which captures both occurrences and co-occurrences of words and senses in a joint probability distribution. This statistical framework lends itself to the task of word sense disambiguation. We evaluate the performance of the model in its participation on the SemEval-2007 coarseand fine-grained all-words tasks under a variety of para...

A data sparseness problem for modeling a language often occurs in many language models (LMs). This problem is caused by the insufficiency of training data, which in turn, makes the infrequent words have unreliable probability. Mapping from words into classes gives the infrequent words more confident probability, because they can rely on other more frequent words in the same class. In this resea...

This paper presents an automatic word sense disambiguation (WSD) system that uses Part-of-Speech (POS) tags along with word classes as the discrete features. Word Classes are derived from the Word Class Assigner using the Word Exchange Algorithm from statistical language processing. Naïve-Bayes classifier is employed from Weka in both the training and testing phases to perform the supervised le...

In our paper we propose a general way of incorporating classbased language models with many-to-many word-to-class mapping into the finite-state transducer (FST) framework. Since class-based models alone usually do not improve the recognition accuracy, we also present a method for an efficient language model combination. An example of a word-to-class mapping based on morphological tags is also g...

In this paper, we present a class-based variable memory length Markov model and its learning algorithm. This is an extension of a variable memory length Markov model. Our model is based on a class-based probabilistic suffix tree, whose nodes have an automatically acquired wordclass relation. We experimentally compared our new model with a word-based bi-gram model, a word-based tri-gram model, a...

The purpose of this study is to determine how the Korean diphthong /jo/ shows phonetic variation in various linguistic environments. pronunciation discussed, focusing on relationship between and distribution range vowels. location a word (monosyllable, word-initial, word-medial, word-final) class (content word, function word) were analyzed using speech 10 female speakers Seoul Corpus. As result...

The present study was an attempt to compare the effect of three VLSs, namely word-part strategy, word-card strategy and context-clue strategy on immediate and delayed English vocabulary retention of Iranian third grade high school students. To this end, 90 students, studying at three high schools in Tabriz, in three intact groups, were considered as the participants of the study. In order to en...