A Context-theoretic Framework for Compositionality in Distributional Semantics

Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts in which it occurs in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings. We relate the framework to existing methods of composing vector-based representations of meaning, and show that our approach generalises many of these, including vector addition, component-wise multiplication, and the tensor product.Comment: Submitted to Computational Linguistics on 20th January 2010 for revie

A Text Analysis of Discourse Semantics of Social Context or Lexicogrammar (An Analysis on Text in Contexct of Meaning Beyond the Clause)

This research deals with finding the content of the text. In this research, writer conducts it in the form of discourse analysis (A text analysis of discourse semantics) on a text that is entitled " A DECADE OF INJUSTICE-TIME TO FIND MUNIR’S REAL KILLERS ", and then writer used descriptive qualitative research .After investigating the data, writer found some findings that the content of the text concerning Munir written by Hidayat it involves matters in relation to the concept of discourse semantics of social context or lexicogrammar  in the context of meaning beyond the clause for the cases (1) interpretating social discourse, it discusses about a framework for discussion where this is as the model of language in social context that has been developed within the broad field of systemic functional linguistics (SFL) that denotes one of  perpectives that will be introduced viz three levels of language: as grammar, as discourse, and as social context, (2) appraisal: negotiating attitude, it is about Negotiating attitude, Kinds of Attitude, Amplifying attitudes, (3) ideation: Representing experience, it pertains about representing experience, sequences of meaning, doing: Ocusing activities, being: Focusing on entities, classifying and describing within elements, (4) conjunction: Connecting events, it extends about the logic of discourse and four kinds of logic, connecting arguments, continuatives, countering our expectations, and (5) identification: tracking participants, it is about keeping track, and who’s who: Identifying people. Thus it can be taken conclusion that  based on some findings about  five matters above and  the content of the text, accordingly it concerns an analysis on text in contexct of meaning beyond the clause which constitutes a part of a text analysis of discourse semantics of social context or lexicogrammar. Keywords: Text analysis, discourse semantics, social context/lexicogrammar, meaning beyond the claus

Spatial Relations in Text-to-Scene Conversion

Spatial relations play an important role in our understanding of language. In particular, they are a crucial component in descriptions of scenes in the world. WordsEye (www.wordseye.com) is a system for automatically converting natural language text into 3D scenes representing the meaning of that text. Natural language offers an interface to scene generation that is intuitive and immediately approachable by anyone, without any special skill or training. WordsEye has been used by several thousand users on the web to create approximately 15,000 fully rendered scenes. We describe how the system incorporates geometric and semantic knowledge about objects and their parts and the spatial relations that hold among these in order to depict spatial relations in 3D scenes

Knowledge-rich Image Gist Understanding Beyond Literal Meaning

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process

The use of typed lambda calculus for comprehension and construction of simulation models in the domain of ecology

We are concerned with two important issues in simulation modelling: model comprehension and model construction. Model comprehension is limited because many important choices taken during the modelling process are not documented. This makes it difficult for models to be modified or used by others. A key factor hindering model construction is the vast modelling search space which must be navigated. This is exacerbated by the fact that many modellers are unfamiliar with the terms and concepts catered to by current tools. The root of both problems is the lack of facilities for representing or reasoning about domain concepts in current simulation technology. The basis for our achievements in both of these areas is the development of a language with two distinct levels; one for representing domain information, and the other for representing the simulation model. Of equal importance, is the fact that we make formal connections between these two levels. The domain we are concerned with is ecological modelling. This language, called Elklogic, is based on the typed lambda calculus. Important features include a rich type structure, the use of various higher order functions, and semantics. This enables complex expressions to be constructed from relatively few primitives. The meaning of each expression can be determined in terms of the domain, the simulation model, or the relationship between the two. We describe a novel representation for sets and substructure, and a variety of other general concepts that are especially useful in the ecological domain. We use the type structure in a novel way: for controlling the modelling search space, rather than a proof search space. We facilitate model comprehension by representing modelling decisions that are embodied in the simulation model. We represent the simulation model separately from, but in terms of a domain mode. The explicit links between the two models constitute the modelling decisions. The semantics of Elklogic enables English text to be generated to explain the simulation model in domain terms

Collecting Semantic Data by Mechanical Turk for the Lexical Knowledge Resource of a Text-to-Picture Generating System

WordsEye is a system for automatically converting natural language text into 3D scenes representing the meaning of that text. At the core of WordsEye is the Scenario-Based Lexical Knowledge Resource (SBLR), a unified knowledge base and representational system for expressing lexical and real-world knowledge needed to depict scenes from text. To enrich a portion of the SBLR, we need to fill out some contextual information about its objects, including information about their typical parts, typical locations and typical objects located near them. This paper explores our proposed methodology to achieve this goal. First we try to collect some semantic information by using Amazon’s Mechanical Turk (AMT). Then, we manually filter and classify the collected data and finally, we compare the manual results with the output of some automatic filtration techniques which use several WordNet similarity and corpus association measures

Anna Wierzbicka, Semantic Decomposition, and the Meaning-Text Approach

The paper aims to demonstrate that the main contribution of Anna Wierzbicka to linguistics is the idea of semantic decomposition - that is, representing meaning in terms of structurally organized configurations of simpler meanings - and a huge amount of specific decompositions of lexical meanings from many languages. One of possible developments of this idea of Wierzbicka’s is the Meaning-Text linguistic approach, and in particular - the Meaning-Text model of natural language. To illustrate the importance and fruitfulness of semantic decomposition, two Meaning-Text mini-models are presented for English and Russian. Two semantically equivalent sentences of these languages are considered: (1) a. Eng. A honeymooner was fatally attacked by a shark. ~ b. Rus. Molodožën pogib v rezul´tate napadenija akuly vo vremja medovogo mesjaca lit. ‘Young.husband died as result of.attack of.shark during honey month’ The formal representations of these sentences at four levels-Meaning-Text style-are shown: semantic, deep-syntactic, surface-syntactic, and deep-morphological. Examples of formal rules relating the representations of two adjacent levels are presented