Natural Language Processing and Knowledge Representation دانشگاه صنعتی اميرکبير (پلي تکنيک تهران) دانشكده مهندسي كامپيوتر آزمايشگاه سيستم‌هاي هوشمند پردازش زبان طبيعي پرهام مرادي

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book Natural Language as Knowledge Representation and Reasoning System This research direction emerged over past few years It grew out of concerns over the efficient handling of large scale, general purpose, reasoning and meaning of natural language Current knowledge representation and reasoning systems do not adequate for NLP This book contains most theoretical and practical computational approaches to representing and utilizing the meaning of natural language

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book Change the role of Natural Language in an Intelligent computer System Traditional view Natural Language as an interface or front end to a system such as an expert system Inferencing and knowledge processing tasks are not part of NLP Current view in this book Natural Language as knowledge representation and reasoning system NLP has own unique, computationally attractive representational and inferential machinery The structure of human mind is close to natural language

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book In the natural-language based knowledge representation and reasoning system Knowledge are : 1- Entered in computer systems via natural language in form of text or dialogs 2- Represent and combined via algorithms and data structures closely simulating the syntax and semantics of natural language 3- Reasoned about inference mechanisms which closely simulate inferences that human make in natural language 4- Exited for computer systems via natural language in the form of natural language answers to queries

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book Authors of the Book : Are computer scientists who focus clearly on the computational, computer science-style aspects of natural language Their ideas have been implemented in existing Some authors present complete systems capable of a particular type of processing on large scale. Some systems are capable of handling large scale corpora of real data

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book Potential for practical applications Combine human knowledge with nonhuman computer capabilities This combination give hope for an in-depth processing of information and knowledge in the huge volumes of natural language inputs.

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Book About the book Section One : Natural language based knowledge representation and reasoning systems based of First order classical logic Section Two : Large scale approaches to representing reasoning with and acquiring different types of knowledge for general-purpose natural language processing systems

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one Five major knowledge representation and reasoning system based on natural language UNO McAllester and Montagovian syntax KRISP Episodic logic SNePS

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one McAllester and Montagovian syntax Based on first order logic Closest to Natural Language as Knowledge representation and reasoning system Closest to Natural Language only for “simple-subject- verb-object sentences” “every doge ate bone ”  “(every dog (ate (some bone)))” Distantly related to the much richer and more complex syntax of actual natural language Effective : polynomial time inference procedure

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one McAllester and Montagovian syntax ( count.. ) Montagovian formula (every dog (ate ( some bone )) ) Is quantifier-free Without quantifying over individual dogs or individual bones all expression denote sets of individuals One set is subset of another set Example : (every dog (ate ( some bone))) is true if : Set of dogs is subset of set of individuals that each of which have the relation denoted by some member of the set of bones.

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one UNO UNO Model Proposed by Iwanska’s Use the Sets for representing syntax and semantic The sentence : “ Jan is a sick, very unhappy woman ” Jane == {[woman (health =>sick, happy => (not happy) (degree => very )) ]} Set of individuals of type Jane is the same as Subset of individuals of the type woman for witch the attribute health has value sick and for witch the function happy yields the value very unhappy

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one SNePS Proposed by Shapiro Uses the sets in several ways First use : Logical connection of SNePS are functions of sets of propositions Example : “squash is an animal, a vegetable or a mineral” SNePS : {animal(squash ), vegetable(squash ), mineral(squash ) }

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one SNePS ( count … ) Second use : predicate the whose i th argument is supposed to be an individual of some type τ, maybe written with set of τ-individuals in i th position, implying the same prediction with any subset of that set in i position Example : Sisters ({Mary, Sue, Sally}) implies : Sisters ({Mary, Sue}) Sisters ({Mary, Sally}) Sisters ({Sue, Sally})

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one 3 theme uses in this methods Sets Individuals Inference rules

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one Individuals McDonald second principle : There should be a first class object type in representation for every class of syntactic category in the language Every well formed expression in the language denotes as individual in the domain. In first-order predicate logic, only terms denote individuals of domain.

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one Some types of Individuals Proposition (episodic logic, SNePS, UNO) Categories (UNO, KRISP, episodic logic) Partially saturated relations (KRISP) Events (KRISP, episodic logic) Action or acts (episodic logic, SNePS) Situation (episodic logic)

دانشگاه صنعتي امير کبير - دانشکده مهندسي کامپيوتر و فناوري اطلاعات - آزمايشگاه سيستم ‌ هاي هوشمند - پردازش زبان طبيعي Introduction to Section one Inference rules Logic specified by : Syntax Semantic Inference Rule For building knowledge representation and reasoning system for natural language based of logic: Specifying particular function symbols Predicate symbols Giving meaning of these symbols Roles of inference in this 5 methods are different with other standard logic