الفهرس 
coverOnly 14 pages are availabe for public view
 |  | from | 96 |  |  |
| | | from | 96 | | |
Abstract
Natural Language Processing enables communication between people and computers. Natural language processing (NLP) is a field of computer science, artificial intelligence and linguistics concerned with the interactions between computers and human (natural) languages. Specifically, it is the process of a computer extracting meaningful information from natural language input and/or producing natural language output. Natural language processing is a very attractive method of human–computer interaction. Natural language understanding is sometimes referred to as an AI-complete problem because it seems to require extensive knowledge about the outside world and the ability to manipulate it. Natural Language Generation (NLG) is sub field of natural language processing which focuses on the generation of written texts in natural language from some underlying semantic representation of information. The objective of natural language generation (NLG) or text generation systems is to produce coherent natural language texts which satisfy a set of one or more communicative goals. To achieve these goals, the generated text should be (among other things). coherent: using well-connected, sensible and comprehensible English; accurate: containing accurate information (or it could lead to the user making false inferences); valid: causing the user to make the desired inferences (for example, telling a naive user that the koala looks like a teddy bear and not telling her that it doesn’t behave like one may result in a nasty surprise); understandable: including information which the user can understand; and relevant: including information which is relevant to the current discourse goal and not redundant. In this thesis, a new model to generate an English text from a recent ontology-based semantic representation called (Rich Semantic Graph) is introduced. The developed model can be exploited in Text Summarization, Machine Translation and Information Retrieval applications. Because of generating multiple texts, the phase of text evaluation is developed in our model to evaluate the final multiple texts based on the most frequently used words using WordNet ontology and the relations between sentences. In this model, WordNet ontology is used to generate multiple texts according to the word synonyms. Also, the model enables users to determine the output text style by selecting one of two writing styles (Cause-Effect and Description-Narration). Finally, the model evaluates the generated texts to rank them based on two criteria: most frequently used words and discourse sentence relations.