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

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

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

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

Case reuse in textual case-based reasoning.

Text reuse involves reasoning with textual solutions of previous problems to solve new similar problems. It is an integral part of textual case-based reasoning (TCBR), which applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. Here, we explore two key research questions in the context of textual reuse: firstly what parts of a solution are reusable given a problem and secondly how might these relevant parts be reused to generate a textual solution. Reasoning with text is naturally challenging and this is particularly so with text reuse. However significant inroads towards addressing this challenge was made possible with knowledge of problem-solution alignment. This knowledge allows us to identify specific parts of a textual solution that are linked to particular problem attributes or attribute values. Accordingly, a text reuse strategy based on implicit alignment is presented to determine textual solution constructs (words or phrases) that needs adapted. This addresses the question of what to reuse in solution texts and thereby forms the first contribution of this thesis. A generic architecture, the Case Retrieval Reuse Net (CR2N), is used to formalise the reuse strategy. Functionally, this architecture annotates textual constructs in a solution as reusable with adaptation or without adaptation. Key to this annotation is the discovery of reuse evidence mined from neighbourhood characteristics. Experimental results show significant improvements over a retrieve-only system and a baseline reuse technique. We also extended CR2N so that retrieval of similar cases is informed by solutions that are easiest to adapt. This is done by retrieving the top k cases based on their problem similarity and then determining the reusability of their solutions with respect to the target problem. Results from experiments show that reuse-guided retrieval outperforms retrieval without this guidance. Although CR2N exploits implicit alignment to aid text reuse, performance can be greatly improved if there is explicit alignment. Our second contribution is a method to form explicit alignment of structured problem attributes and values to sentences in a textual solution. Thereafter, compositional and transformational approaches to text reuse are introduced to address the question of how to reuse textual solutions. The main idea in the compositional approach is to generate a textual solution by using prototypical sentences across similar authors. While the transformation approach adapts the retrieved solution text by replacing sentences aligned to mismatched problem attributes using sentences from the neighbourhood. Experiments confirm the usefulness of these approaches through strong similarity between generated text and human references. The third and final contribution of this research is the use of Machine Translation (MT) evaluation metrics for TCBR. These metrics have been shown to correlate highly with human expert evaluation. In MT research, multiple human references are typically used as opposed to a single reference or solution per test case. An introspective approach to create multiple references for evaluation is presented. This is particularly useful for CBR domains where single reference cases (or cases with a single solution per problem) typically form the casebase. For such domains we show how multiple references can be generated by exploiting the CBR similarity assumption. Results indicate that TCBR systems evaluated with these MT metrics are closer to human judgements