It is well-known that there are polysemous words like sentence whose "meaning" or "sense" depends on the context of use. We have recently reported on two new word-sense disambiguation systems, one trained on bilingual material (the Canadian Hansards) and the other trained on monolingual material (Roget's Thesaurus and Grolier's Encyclopedia). As this work was nearing completion, we observed a v...

The paper presents an experiment in which synsets for Slovene wordnet were induced automatically from several multilingual resources. Our research is based on the assumption that translations are a plausible source of semantically relevant information. More specifically, we argue that the translational relation on the one hand reduces ambiguity of a source word and on the other conveys semantic...

The problem of Word Sense Disambiguation (WSD) can be defined as the task of assigning the most appropriate sense to the polysemous word within a given context. Many supervised, unsupervised and semi-supervised approaches have been devised to deal with this problem, particularly, for the English language. However, this is not the case for Hindi language, where not much work has been done. In th...

Two eye-tracking experiments investigated the processing of mass nouns used as count nouns and count nouns used as mass nouns. Following Copestake and Briscoe (1995), the basic or underived sense of a word was treated as the input to a derivational rule (“grinding” or “portioning”) which produced the derived sense as output. It was hypothesized that in the absence of biasing evidence readers wo...

Aspect-context sentiment classification aims to classify the sentiments about an aspect that corresponds its context. Typically, machine learning models considers and context separately. They do not execute in parallel. To model contexts aspects separately, most of methods with attention mechanisms typically employ Long Short Term Memory network approach. Attention mechanisms, on other hand, ta...

Chinese short text matching is a fundamental task in natural language processing. Existing approaches usually take characters or words as input tokens. They have two limitations: 1) Some are polysemous, and semantic information not fully utilized. 2) models suffer potential issues caused by word segmentation. Here we introduce HowNet an external knowledge base propose Linguistic Enhanced graph ...

Unsupervised word representations have demonstrated improvements in predictive generalization on various NLP tasks. Much effort has been devoted to effectively learning word embeddings, but little attention has been given to distributed character representations, although such character-level representations could be very useful for a variety of NLP applications in intrinsically “character-base...

Exploiting semantic content of texts due to its wide range of applications such as finding related documents to a query, document classification and computing semantic similarity of documents has always been an important and challenging issue in Natural Language Processing. In this paper, using Wikipedia corpus and organizing it by three-dimensional tensor structure, a novel corpus-based approa...

Current state-of-the-art Word Sense Disambiguation (WSD) algorithms are mostly supervised and use the P (Sense|Word) statistic for annotation. This P (Sense|Word) statistic is obtained after training the model on an annotated corpus. The performance of WSD algorithms do not match the efficiency and quality of human annotation. It is therefore important to know the role of the contextual clues i...