The Representation and Processing of Compound Words

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1 University Press Scholarship Online You are looking at 1-8 of 8 items for: keywords : natural language processing The Representation and Processing of Compound Words Gary Libben and Gonia Jarema (eds) Published in print: 2007 Published Online: January 2010 ISBN: eisbn: Item type: book Publisher: Oxford University Press DOI: / acprof:oso/ This book presents new work on the psycholinguistics and neurolinguistics of compound words. It shows the insights this work offers on natural language processing and the relation between language, mind, and memory. Compounding is an easy and effective way to create and transfer meanings. By building new lexical items based on the meanings of existing items, compounds can usually be understood on first presentation, though as, for example, breadboard, cardboard, cupboard, and sandwich-board show the rules governing the relations between the components meanings are not always straightforward. Compound words may be segmentable into their constituent morphemes in much the same way as sentences can be divided into their constituent words: children and adults would not otherwise find them interpretable. But compound sequences may also be independent lexical items that can be retrieved for production as single entities and whose idiosyncratic meanings are stored in the mind. Compound words reflect the properties both of linguistic representation in the mind and of grammatical processing. They thus offer opportunities for investigating key aspects of the mental operations involved in language: for example, the interplay between storage and computation; the manner in which morphological and semantic factors impact on the nature of storage; and the way the mind s computational processes serve on-line language comprehension and production. This book explores the nature of these opportunities, assesses what is known, and considers what may yet be discovered and how. Page 1 of 5

2 A Minimalist Parsing Model for Codeswitching Edward P. Stabler and Jeff MacSwan in Grammatical Theory and Bilingual Codeswitching Published in print: 2014 Published Online: May 2016 ISBN: eisbn: mitpress/ A fully lexicalized grammar can represent the knowledge of a multilingual speaker simply by putting lexical items from the various languages together. This conception suggests that multilingualism should be a quite natural state, an idea that fits well with a conception according to which every adjustment in register or dialect for context is regarded as a kind of codeswitching (CS). This conception of CS does not invoke any special mechanisms to control the interaction among the languages a learner knows. Nonetheless, some restrictions appear to be needed, since CS does not occur at arbitrary positions in an utterance. Exploring these restrictions, MacSwan (1999, 2014) proposes that the syntactic elements of various languages can mix freely, but the morphophonologies have different properties and cannot be mixed. Hence, CS is possible only at those points in an utterance that would not disrupt morphophonological dependencies. A computational model of language analysis proposed by Stabler (2001), which separates morphophonological and syntactic processing, is explicit and general enough to allow these CS proposals to be realized. This chapter shows such a computational model at work, concluding that (1) when the syntax is lexicalized, so all crosslinguistic variation is due to the lexicon, CS is immediately predicted for mixed utterances; (2) the extent of codeswitching in the syntax is based on the extent to which the categories of the different languages are assimilated, which allows for the influence of learned CS patterns; and (3) morphophonology, based on linear position and ranked constraints, does not allow word-internal CS. Learning to interpret spatial natural language in terms of qualitative spatial relations * Parisa Kordjamshidi, Joana Hois, and Marie-Francine Moens in Representing Space in Cognition: Interrelations of behaviour, language, and formal models Published in print: 2013 Published Online: January 2014 ISBN: eisbn: Publisher: Oxford University Press DOI: / acprof:oso/ Page 2 of 5

3 Computational approaches in spatial language understanding nowadays distinguish and use different aspects of spatial and contextual information. These aspects comprise linguistic grammatical features, qualitative formal representations, and situational context-aware data. In this chapter, we apply formal models and machine learning techniques to map spatial semantics in natural language to qualitative spatial representations. In particular, we investigate whether and how well linguistic features can be classified and automatically extracted and mapped to region-based qualitative relations using corpus-based learning. We separate the challenge of spatial language understanding into two tasks: (i) we identify and automatically extract those parts from linguistic utterances that provide specifically spatial information, and (ii) we map the extracted parts that result from the first task to qualitative spatial representations. In this chapter, we present both tasks and we particularly discuss experimental results of the second part of mapping linguistic features to qualitative spatial relations. Our results show that region-based spatial relations can indeed be learned to a high degree and that they are distinguishable on the basis of different linguistic features. Language Florian Cramer in Software Studies: A Lexicon Published in print: 2008 Published Online: ISBN: eisbn: mitpress/ This chapter describes the detailed relationship software and language; it states that software processes the natural language and constructs in programming languages. The chapter explores the in-depth analysis of software implementation languages and software written languages, and provides a discussion on the role of computer control languages, machine languages, common human languages, and different language variants. It concludes with the differentiation between the computer programming languages and coding concepts in the computing sector. Efficient Processing with CCG Mark Steedman in Taking Scope: The Natural Semantics of Quantifiers Published in print: 2011 Published Online: ISBN: eisbn: mitpress/ Page 3 of 5

4 Combinatory Categorial Grammar (CCG) allows semantically equivalent alternate surface derivations typified by the sentence Harry admires Louise, as well as English noun phrases, to have all of the type-raised categories allowed by a full-blown morphological case system. Many critics have argued that this so-called spurious ambiguity makes CCG quite impracticable to apply to useful tasks such as parsing and question answering in open domains, regardless of its linguistic attractions. This chapter examines how CCG can be used for efficient natural language processing. It first considers algorithms that have formed the basis of a number of practical CCG parsers before turning to logical forms and how they are built with CCG. The chapter also discusses processing scope and pronominal reference in CCG, generation of strings from logical forms using CCG, the use of scope for rapid inference in support of question answering or textual entailment, and human sentence processing. Prologue Mark Steedman in Taking Scope: The Natural Semantics of Quantifiers Published in print: 2011 Published Online: ISBN: eisbn: mitpress/ Computational linguists and psycholinguists show little interest in the formal semantics of quantification, even though quantifiers and other scoping elements play an important role in human and computational natural language processing. Quantifiers, along with negation and other categories such as modality, tense, and aspect, matter because they support inference and entailment. To understand the relevance of quantifier-scope, it is necessary to look at how rare events reveal the nature of the system and where quantifers might actually have practical applications. Questions provide few opportunities to deploy quantifiers, other than simple definites and indefinites. Some quantifiers convey subtle implicatures about the completeness or otherwise of the speaker s knowledge. Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation Masashi Sugiyama and Motoaki Kawanabe Published in print: 2012 Published Online: September 2013 ISBN: eisbn: mitpress/ Page 4 of 5

5 Item type: book As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with nonstationarity is one of modern machine learning s greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the stateof-the-art research in the field, the book discusses topics that include learning under covariate shift, model selection, importance estimation, and active learning. It describes such real-world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. Applications of Covariate Shift Adaptation Masashi Sugiyama and Motoaki Kawanabe in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation Published in print: 2012 Published Online: September 2013 ISBN: eisbn: mitpress/ This chapter discusses state-of-the-art applications of covariate shift adaptation techniques to various real-world problems. It covers non-stationarity adaptation in brain-computer interfaces; speaker identification through change in voice quality; domain adaptation in natural language processing; age prediction from face images under changing illumination conditions; user adaptation in human activity recognition; and efficient sample reuse in autonomous robot control. Page 5 of 5