Semantic similarity aims at establishing resemblance by interpreting the meaning of the objects being compared. The Semantic Web can benefit from semantic similarity in several ways: ontology alignment and merging, automatic ontology construction, semantic-search, to cite a few. Current approaches mostly focus on computing similarity between nouns. The aim of this paper is to define a framework...

The parts of speech disambiguation in corpora is most challenging area in Natural Language Processing. However, someworkshave been done in the past to overcome the problem of bilingual corpora disambiguation forHindi using Hidden Markov Model and Neural Network. In this paper,Quantum Neural Network (QNN) forHindi parts of speech tagger has been used.To analyze the effectiveness of the proposed ...

In this paper we describe a compression-based authorship verification model used in conjunction with a parts of speech tagger. We use standard language-specific parts of speech taggers on the texts in question to generate a stream of symbols representing each word, run the PPM (Prediction by Partial Matching) algorithm on the resulting stream, and use Bobicev’s method of calculating and compari...

Part of speech (POS) tagging is the process of assigning the part of speech tag or other lexical class marker to each and every word in a sentence. In many Natural Language Processing applications such as word sense disambiguation, information retrieval, information processing, parsing, question answering, and machine translation, POS tagging is considered as the one of the basic necessary tool...

Part of speech (POS) tagging is basically the process of automatically assigning its lexical category to each word according to its context and definition. Each word of sentence is marked in croups as corresponding to a particular part of speech like noun, verb, adjective and adverb. POS serves as a first step in natural language process applications like information extraction, parsing, and wo...

We present a universal Parts-of-Speech (POS) tagset framework covering most of the Indian languages (ILs) following the hierarchical and decomposable tagset schema. In spite of significant number of speakers, there is no workable POS tagset and tagger for most ILs, which serve as fundamental building blocks for NLP research. Existing IL POS tagsets are often designed for a specific language; th...

In this paper, four state-of-art probabilistic taggers i.e. TnT tagger, TreeTagger, RF tagger and SVM tool, are applied to the Urdu language. For the purpose of the experiment, a syntactic tagset is proposed. A training corpus of 100,000 tokens is used to train the models. Using the lexicon extracted from the training corpus, SVM tool shows the best accuracy of 94.15%. After providing a separat...

Parts of Speech Tagger (POS) is the task of assigning to each word of a text the proper POS tag in its context of appearance in sentences. The Chunking is the process of identifying and assigning different types of phrases in sentences. In this paper, a statistical approach with the Hidden Markov Model following the Viterbi algorithm is described. The corpus both tagged and untagged used for tr...