الفهرس 
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Abstract
Variability of semantic expression is a fundamental phenomenon of a natural
language where same meaning can be expressed by different texts. The process of
inferring a text from another is called Textual Entailment. Textual Entailment is
useful in a wide range of applications, including question answering, summarization,
text generation, and machine translation. The textual entailment Recognition is one
of the recent challenges of the Natural Language Processing (NLP) domain.
Textual entailment (TE) is a relation that holds between two pieces of text, where if
one read the first piece can conclude that the second is most likely true. It can help in
various natural language processing (NLP) applications as explained above. So,
researches on textual entailment attracted a significant amount of attention in recent
years. Researchers have worked on Recognizing Textual Entailment (RTE), since
2005 [1].
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The main objective of this thesis is to propose an effective model to Textual
Entailment Recognition, such that given a sentence pair (a, b), the task is to predict
whether b is entailed or not by a.
A spectrum of approaches has been proposed for Recognizing Textual Entailment
(RTE). Most of RTE systems are. based on Machine Learning, lexical or semantic
approaches [2]. However, the entailment decision problem can be considered as a
classification problem. Such systems use features such as lexical, syntactic and
semantic features.
The proposed Textual Entailment Recognition model is based on hybrid approach
which consists of Preprocessing, lexical, syntactic, semantic analysis and Deep
Learning classifier entailment module.
The proposed model consists from 5 components, it starts with preprocessing
component and ends with Deep learning classifier, between them there are lexical,
syntactic and semantic analysis components. Some of those components can be
separated and used in textual entailment recognition solely.
Lexical analysis is used only a lexical representation of the sentences to compare
lexical items. It is based on lexicographic matching between terms.