Learning for semantic parsing using statistical syntactic parsing techniques

textNatural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that it sometimes is referred to as an AI-complete problem, implying that its difficulty is equivalent to solving the central artificial intelligence problem -- making computers as intelligent as people. Despite its complexity, natural language understanding continues to be a fundamental problem in natural language processing in terms of its theoretical and empirical importance. In recent years, startling progress has been made at different levels of natural language processing tasks, which provides great opportunity for deeper natural language understanding. In this thesis, we focus on the task of semantic parsing, which maps a natural language sentence into a complete, formal meaning representation in a meaning representation language. We present two novel state-of-the-art learned syntax-based semantic parsers using statistical syntactic parsing techniques, motivated by the following two reasons. First, the syntax-based semantic parsing is theoretically well-founded in computational semantics. Second, adopting a syntax-based approach allows us to directly leverage the enormous progress made in statistical syntactic parsing. The first semantic parser, Scissor, adopts an integrated syntactic-semantic parsing approach, in which a statistical syntactic parser is augmented with semantic parameters to produce a semantically-augmented parse tree (SAPT). This integrated approach allows both syntactic and semantic information to be available during parsing time to obtain an accurate combined syntactic-semantic analysis. The performance of Scissor is further improved by using discriminative reranking for incorporating non-local features. The second semantic parser, SynSem, exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. This pipeline approach allows semantic parsing to conveniently leverage the most recent progress in statistical syntactic parsing. We report experimental results on two real applications: an interpreter for coaching instructions in robotic soccer and a natural-language database interface, showing that the improvement of Scissor and SynSem over other systems is mainly on long sentences, where the knowledge of syntax given in the form of annotated SAPTs or syntactic parses from an existing parser helps semantic composition. SynSem also significantly improves results with limited training data, and is shown to be robust to syntactic errors.Computer Science

KONTROL PERILAKU AGEN MENGGUNAKAN FUZZY LOGIC BERBASIS SEMANTIK

Natural language are model computation process which created by language, so it can make interaction between human and computer becomes easier. This model computation model is very useful for scientific needs such as research natural language for human life. All knowledge subject which have relation with natural language processing includes are : Fonetic and fonology, morfology, syntax, semantic, pragmatic, discourse knowledge, and world knowledge. The definition of semantic are mapping syntax structur with using each word into more basic structural and it is not depend on sentence structure. Semantic are learn about the meaning of any words and how this words can translate any complete sentence. Semantic analysis process is used to recognize the words that pass away and have relation with the word in domain. This process works to connect the syntax structure from word, phrase, sentence, due to paragraph At Previous research which have any relation with semantic mapping and fuzzy logic, semantic mapping works depend on physically display and then displaying any model / character role in story. Based on fuzzy logic measurement and agent conditional graph, agent who had energy between 75 ' 100 % had speed value more constant than agent with energy left 50% or less than 50%. Key word : Semantic, mySQL, fuzzy logic, agent

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered. Connections with other branches of linguistics, especially psycholinguistics, sociolinguistics, historical linguistics, and natural language processing, are explored. The book will be of interest to scholars in philosophy, linguistics, artificial intelligence, and cognitive science. It should also interest readers in related fields like literary and cultural theory and the social sciences

Predicate logic unplugged

this paper we describe the syntax and semantics of a description language for underspecified semantic representations. This concept is discussed in general and in particular applied to Predicate Logic and Discourse Representation Theory. The reason for exploring underspecified representations as suitable semantic representations for natural language expressions emerges directly from practical natural language processing applications. The so-called Combinatorial Explosion Puzzle, a well known problem in this area, can succesfully be tackled by using underspecified representations. The source of this problem, scopal ambiguities in natural language expressions, is discussed in section 2. The core of the paper presents Hole Semantics. This is a general proposal for a framework, in principle suitable for any logic, where underspecified representations play a central role. There is a clear separation between the object language (the logical language one is interested in) and the meta language (the language that describes and interprets underspecified structures). It has been noted by various authors that the meaning of an underspecified semantic representation cannot be expressed in terms of a disjunction of denotations, but rather as a set of denotations (cf. Poesio 1994). We support this view, and use it as underlying principle for the definition of the semantic interpretation function of underspecified structures. Section 3 is an informal introduction to Hole Semantics, and in section 4 things are formally defined. In section 5 we apply Hole Semantics to Predicate Logic, resulting in an "unplugged" version of (static and dynamic) Predicate Logic. In section 6 we show that this idea easily carries over to Discourse Representation Structures. A lot of attention has been paid..

Cognitive Computation sans Representation

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing