In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient...

The bag-of-words (BoW) method has been used widely in language modelling and information retrieval. A document is expressed as a group of words disregarding the grammar and the order of word information. A typical BoW method is latent semantic analysis (LSA), which maps the words and documents onto the vectors in LSA space. In this paper, the concept of BoW is extended to Bag-of-Word Pairs (BoW...

In this paper, we propose a method that employs sentences similarities from context word embeddings for supervised word sense disambiguation. In particular, if N example sentences exist in training data, an N-dimensional vector with N similarities between each pair of example sentences is added to a basic feature vector. This new feature vector is used to train a classifier and identification. ...

Given a target word wi to be disambiguated, we define a class of local contexts for wi such that the sense of wi is univocally determined. We call such local contexts sense discriminative and represent them with sense discriminative (SD) patterns of lexico-syntactic features. We describe an algorithm for the automatic acquisition of minimal SD patterns based on training data in SemCor. We have ...

1. The runs differ in the types of visual features used. All runs use several bag-of-word representations fed to separate linear SVMs and the SVMs were fused by logistic regression. *F_A_Brno_resource_4: Only single best visual features (on the training set) are used – dense image sampling with rgb-SIFT. * F_A_Brno_basic_3: This run uses dense sampling and Harris-Laplace detector in combination...

This paper presents a series of experiments in applying compositional distributional semantic models to dialogue act classification. In contrast to the widely used bag-ofwords approach, we build the meaning of an utterance from its parts by composing the distributional word vectors using vector addition and multiplication. We investigate the contribution of word sequence, dialogue act sequence,...

Given a target word wi to be disambiguated, we define a class of local contexts for wi such that the sense of wi is univocally determined. We call such local contexts sense discriminative and represent them with sense discriminative (SD) patterns of lexico-syntactic features. We describe an algorithm for the automatic acquisition of minimal SD patterns based on training data in SemCor. We have ...

We present results that show that incorporating lexical and structural semantic information is effective for word sense disambiguation. We evaluated the method by using precise information from a large treebank and an ontology automatically created from dictionary sentences. Exploiting rich semantic and structural information improves precision 2–3%. The most gains are seen with verbs, with an ...