due to the explosive growth of the world-wide web, automatictext summarization has become an essential tool for web users. in this paperwe present a novel approach for creating text summaries. using fuzzy logicand word-net, our model extracts the most relevant sentences from an originaldocument. the approach utilizes fuzzy measures and inference on theextracted textual information from the docu...

Due to the explosive growth of the world-wide web, automatictext summarization has become an essential tool for web users. In this paperwe present a novel approach for creating text summaries. Using fuzzy logicand word-net, our model extracts the most relevant sentences from an originaldocument. The approach utilizes fuzzy measures and inference on theextracted textual information from the docu...

Natural Language Processing is introducing a new era in the field of Computer Science and Machine translation. HumanMachine interaction is to play a very important role in the coming centuries as the dependency of human over the machine is increasing variably. Word-Net was first introduced by Miller and Fellbaum in 1985. WordNet is a Lexical database for the Human Languages. It groups the Human...

Word similarity is broadly used in many applications, such as information retrieval, information extraction, text classification, word sense disambiguation, example-based machine translation, etc. There are two different methods used to compute similarity: one is based on ontology or a semantic taxonomy; the other is based on collocations of words in a corpus. As a lexical knowledgebase with ri...

This paper presents Edge-Guided (E-G), an optimized text preprocessing technique for compression purposes. It transforms the original text into a word net, which stores all relationships between adjoining words. A specific directed graph is proposed to model this transformation: words are stored in vertices, whereas edges represent word transitions. Thus, the word net has a text representation ...

Search engines perform the task of retrieving information related to the user-supplied query words. This task has two parts; one is finding ’featured words’ which describe an article best and the other is finding a match among these words to user-defined search terms. There are two main independent approaches to achieve this task. The first one, using the concepts of semantics, has been impleme...

A neural network model of word prediction based on automatically derived corpus-based term vectors is proposed as a replacement for the standard n-gram model. Initial testing and evaluation show the technique is promising, but more rigorous evaluation techniques are needed.