A connectionist account of the emergence of the literal-metaphorical-anomalous distinction in young children

We present the first developmental computational model of metaphor comprehension, which seeks to relate the emergence of a distinction between literal and non-literal similarity in young children to the development of semantic representations. The model gradually learns to distinguish literal from metaphorical semantic juxtapositions as it acquires more knowledge about the vehicle domain. In accordance with Keil (1986), the separation of literal from metaphorical comparisons is found to depend on the maturity of the vehicle concept stored within the network. The model generates a number of explicit novel predictions

Metaphor as categorisation: a connectionist implementation

A key issue for models of metaphor comprehension is to explain how in some metaphorical comparison , only some features of B are transferred to A. The features of B that are transferred to A depend both on A and on B. This is the central thrust of Black's well known interaction theory of metaphor comprehension (1979). However, this theory is somewhat abstract, and it is not obvious how it may be implemented in terms of mental representations and processes. In this paper we describe a simple computational model of on-line metaphor comprehension which combines Black's interaction theory with the idea that metaphor comprehension is a type of categorisation process (Glucksberg & Keysar, 1990, 1993). The model is based on a distributed connectionist network depicting semantic memory (McClelland & Rumelhart, 1986). The network learns feature-based information about various concepts. A metaphor is comprehended by applying a representation of the first term A to the network storing knowledge of the second term B, in an attempt to categorise it as an exemplar of B. The output of this network is a representation of A transformed by the knowledge of B. We explain how this process embodies an interaction of knowledge between the two terms of the metaphor, how it accords with the contemporary theory of metaphor stating that comprehension for literal and metaphorical comparisons is carried out by identical mechanisms (Gibbs, 1994), and how it accounts for both existing empirical evidence (Glucksberg, McGlone, & Manfredi, 1997) and generates new predictions. In this model, the distinction between literal and metaphorical language is one of degree, not of kind

On the role of metaphor in creative cognition

Abstract. We consider some examples of creativity in a number of diverse cognitive domains like art, science, mathematics, product development, legal reasoning, etc. to articulate an operational account of creative cognition. We present a model of cognition that explains how metaphor creates new insights into an object or a situation. The model is based on assuming that cognition invariably leads to a loss of information and that metaphor can recover some of this lost information. In this model we also contrast the role of traditional analogy (mapping based on existing conceptualization) with the role of metaphor (destroying existing conceptualizations in order to create new conceptualizations)

Validating a group oral task in a university entry test:Interactional competence as a target construct in an academic context

Increasing seminar-style teaching and assessment diversification in higher education mean that group assessments have become part of degree assessments in many university contexts. In principle, therefore, group oral tasks seem a meaningful task type for university entry tests. However, limited research is available on the validity of such tasks, particularly for local university entrance tests such as the Malaysian University English Test (MUET), of which the scores are used to demonstrate meeting Malaysian university entry requirements. Therefore, this study investigated the interactional features elicited during a group oral task for a university entry test (MUET) and compared them to the interactional features of group oral assessments in the target domain (degree-level study), to shed light on the context validity of MUET’s group oral task. To gain insights into the construct tested by the MUET group oral task versus by degree-level group assessments, video recordings were made of four MUET simulation tests and of two groups of first-year and two groups of final-year students completing assessed group academic discussions in an English language and an IT course, respectively. After transcribing the recordings, Applied Conversation Analysis was conducted to identify the interactional features during group oral performances in the three settings (MUET, English course, IT course). The analyses of the talk revealed differences between the turn and topic management features of the group oral performances in the three contexts with a focus on MUET versus English and IT, and to a lesser extent between groups within the same context. More specifically, differences were found in general features of turn-taking (e.g., organisation of talk in terms of specific strategies used during the initiation, maintaining, and ending of the group interaction), turn length, number of turns, and turn allocations. Topic management features such as opening, topic initiation, extension and closing also differed. In addition, an evaluation of the MUET materials by a group of ten expert language testers suggested that the MUET group interaction task did not lend itself to eliciting all the intended features listed in the MUET test specifications and rating scale. All in all, these findings indicate important shortcomings to the validity of the MUET group oral task. The significance of the study lies in the insights gained into the context validity of the MUET group interaction task and their implications for this high-stakes test. The study also offers insights into the nature of turn and topic management microfeatures in academic group interactions more generally

Mystifying discourse: a critique of current assumptions and an alternative framework for analysis

The thesis is concerned with texts that mystify events being reported. It begins by focusing\ud on Critical Discourse Analysis (CDA), a currently prominent enterprise, one of whose\ud concerns is with the isolation of text which mystifies the nature of events described. When\ud CDA isolates mystifying text, it is usually with the perspective of a non-analytical reader,\ud either explicitly or implicitly in mind. However, the notion of a non-analytical reader in\ud CDA is undeveloped from a cognitive point of view. The general structure of the thesis is\ud as follows. In the first section, I show how CDA's approach to highlighting textual\ud mystification is inadvertently bound up with symbolic notions of mental representation in\ud cognitive science. In the second section, I outline theories of mental representation in\ud connectionism and cognitive linguistics which problematise the symbolic assumptions of\ud CDA and thus what CDA locates as mystifying text. The thesis develops cumulatively\ud towards an alternative framework for highlighting mystification, in the third section, which\ud includes compatible elements from connectionism, cognitive linguistics and recent\ud psycholinguistic research on inference generation. My framework predicts how certain text\ud can lead to mystification for a non-analytical reader who has little vested interest in a text\ud and is largely unfamiliar with its subject matter. I show how mystification for this nonanalytical\ud reader is connected with inference generation but, in contrast to CDA, I provide\ud a detailed processing profile for such a reader. Attitudes in CDA towards inference\ud generation are often inconsistent and are in conflict with recent psycholinguistic research.\ud My framework, rooted in empirical psycholinguistic study, enables a more plausible,\ud comprehensive and thus consistent perspective on inference generation in reading and how\ud this relates to mystification. Finally, my framework also highlights CDA's 'overinterpretation'\ud in text exegesis done by proxy for non-analytical readers

Mystifying discourse: a critique of current assumptions and an alternative framework for analysis

The thesis is concerned with texts that mystify events being reported. It begins by focusing on Critical Discourse Analysis (CDA), a currently prominent enterprise, one of whose concerns is with the isolation of text which mystifies the nature of events described. When CDA isolates mystifying text, it is usually with the perspective of a non-analytical reader, either explicitly or implicitly in mind. However, the notion of a non-analytical reader in CDA is undeveloped from a cognitive point of view. The general structure of the thesis is as follows. In the first section, I show how CDA's approach to highlighting textual mystification is inadvertently bound up with symbolic notions of mental representation in cognitive science. In the second section, I outline theories of mental representation in connectionism and cognitive linguistics which problematise the symbolic assumptions of CDA and thus what CDA locates as mystifying text. The thesis develops cumulatively towards an alternative framework for highlighting mystification, in the third section, which includes compatible elements from connectionism, cognitive linguistics and recent psycholinguistic research on inference generation. My framework predicts how certain text can lead to mystification for a non-analytical reader who has little vested interest in a text and is largely unfamiliar with its subject matter. I show how mystification for this nonanalytical reader is connected with inference generation but, in contrast to CDA, I provide a detailed processing profile for such a reader. Attitudes in CDA towards inference generation are often inconsistent and are in conflict with recent psycholinguistic research. My framework, rooted in empirical psycholinguistic study, enables a more plausible, comprehensive and thus consistent perspective on inference generation in reading and how this relates to mystification. Finally, my framework also highlights CDA's 'overinterpretation' in text exegesis done by proxy for non-analytical readers

Case Retrieval Nets as a Model for Building Flexible Information Systems

Im Rahmen dieser Arbeit wird das Modell der Case Retrieval Netze vorgestellt, das ein Speichermodell für die Phase des Retrievals beim fallbasierten Schliessen darstellt. Dieses Modell lehnt sich an Assoziativspeicher an, insbesondere wird das Retrieval als Rekonstruktion des Falles betrachtet anstatt als eine Suche im traditionellen Sinne. Zwei der wesentlichen Vorteile des Modells sind Effizienz und Flexibilität: Effizienz beschreibt dabei die Fähigkeit, mit grossen Fallbasen umzugehen und dennoch schnell ein Resultat des Retrievals liefern zu können. Im Rahmen dieser Arbeit wird dieser Aspekt formal untersucht, das Hauptaugenmerk ist aber eher pragmatisch motiviert insofern als der Retrieval-Prozess so schnell sein sollte, dass der Benutzer möglichst keine Wartezeiten in Kauf nehmen muss. Flexibilität betrifft andererseits die allgemeine Anwendbarkeit des Modells in Bezug auf veränderte Aufgabenstellungen, auf alternative Formen der Fallrepräsentation usw. Hierfür wird das Konzept der Informationsvervollständigung diskutiert, welches insbesondere für die Beschreibung von interaktiven Entscheidungsunterstützungssystemen geeignet ist. Traditionelle Problemlöseverfahren, wie etwa Klassifikation oder Diagnose, können als Spezialfälle von Informationsvervollständigung aufgefasst werden. Das formale Modell der Case Retrieval Netze wird im Detail erläutert und dessen Eigenschaften untersucht. Anschliessend werden einige möglich Erweiterungen beschrieben. Neben diesen theoretischen Aspekten bilden Anwendungen, die mit Hilfe des Case Retrieval Netz Modells erstellt wurden, einen weiteren Schwerpunkt. Diese lassen sich in zwei grosse Richtungen einordnen: intelligente Verkaufsunterstützung für Zwecke des E-Commerce sowie Wissensmanagement auf Basis textueller Dokumente, wobei für letzteres der Aspekt der Wiederbenutzung von Problemlösewissen essentiell ist. Für jedes dieser Gebiete wird eine Anwendung im Detail beschrieben, weitere dienen der Illustration und werden nur kurz erläutert. Zuvor wird allgemein beschrieben, welche Aspekte bei Entwurf und Implementierung eines Informationssystems zu beachten sind, welches das Modell der Case Retrieval Netze nutzt.In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable. Flexibility, on the other hand, is related to the general applicability of a case memory depending on the type of task to perform, the representation of cases etc. For this, the concept of information completion is discussed which allows to capture the interactive nature of problem solving methods in particular when they are applied within a decision support system environment. As discussed, information completion, thus, covers more specific problem solving types, such as classification and diagnosis. The formal model of CRNs is presented in detail and its properties are investigated. After that, some possible extensions are described. Besides these more theoretical aspects, a further focus is set on applications that have been developed on the basis of the CRN model. Roughly speaking, two areas of applications can be recognized: electronic commerce applications for which Case-Based Reasoning may provide intelligent sales support, and knowledge management based on textual documents where the reuse of problem solving knowledge plays a crucial role. For each of these areas, a single application is described in full detail and further case studies are listed for illustration purposes. Prior to the details of the applications, a more general framework is presented describing the general design and implementation of an information system that makes uses of the model of CRNs

An investigation into figurative language in the ‘LOLITA' NLP system

The classical and folk theory view on metaphor and figurative language assumes that metaphor is a rare occurrence, restricted to the realms of poetry and rhetoric. Recent results have, however, unarguably shown that figurative language of various complexity exhibits great systematicity and is pervasive in everyday language and texts. If the ubiquity of figurative language cannot be disputed, however, any natural language processing (NLP) system aiming at processing text beyond a restricted scope has to be able to deal with figurative language. This is particularly true if the processing is to be based on deep techniques, where a deep analysis of the input is performed. The LOLITA NLP system employs deep techniques and, therefore, must be capable of dealing with figurative input. The task of natural language (NL) generation is affected by the naturalness of figurative language, too. For if metaphors are frequent and natural, NL generation not capable of handling figurative language will seem restricted and its output unnatural. This thesis describes the work undertaken to examine the options for extending the LOLITA system in the direction of figurative language processing and the results of this project. The work critically examines previous approaches and their contribution to the field, before outlining a solution which follows the principles of natural language engineering

Localist representation can improve efficiency for detection and counting

Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility

Parser of emotion metaphors

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (leaves 77-80).Although metaphor is generally recognized as an integral component of everyday language, very few computational systems capable of understanding metaphoric utterances exist today. This thesis describes one approach to the problem and presents PoEM, a prototype system which recognizes and interprets metaphoric descriptions of emotions and mental states in single-sentence input. Building upon previous work in knowledge-based metaphor comprehension, this research adopts a goal-driven approach which assumes each metaphor is selected by a speaker for its aptness at serving a particular communicative goal. To identify these goals, an empirical analysis of metaphor distribution in song lyrics was performed, and typical communicative intentions and surface patterns were identified for the top five most frequently occurring metaphor groups. These intentions and surface patterns have been implemented as a set of metaphor templates and interpretation rules in PoEM, using the WordNet lexical database for supplemental semantic information. Evaluation of PoEM demonstrates fairly high accuracy but low recall.Laurie Satsue Hiyakumoto.S.M