41 research outputs found

    Licensing german negative polarity items in LTAG

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    Our paper aims at capturing the distribution of negative polarity items (NPIs) within lexicalized Tree Adjoining Grammar (LTAG). The condition under which an NPI can occur in a sentence is for it to be in the scope of a negation with no quantifiers scopally intervening. We model this restriction within a recent framework for LTAG semantics based on semantic unification. The proposed analysis provides features that signal the presence of a negation in the semantics and that specify its scope. We extend our analysis to modelling the interaction of NPI licensing and neg raising constructions

    Constraint-based computational semantics : a comparison between LTAG and LRS

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    This paper compares two approaches to computational semantics, namely semantic unification in Lexicalized Tree Adjoining Grammars (LTAG) and Lexical Resource Semantics (LRS) in HPSG. There are striking similarities between the frameworks that make them comparable in many respects. We will exemplify the differences and similarities by looking at several phenomena. We will show, first of all, that many intuitions about the mechanisms of semantic computations can be implemented in similar ways in both frameworks. Secondly, we will identify some aspects in which the frameworks intrinsically differ due to more general differences between the approaches to formal grammar adopted by LTAG and HPSG

    A Type-Theoretic Account of Neg-Raising Predicates in Tree Adjoining Grammars

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    International audienceNeg-Raising (NR) verbs form a class of verbs with a clausal complement that show the following behavior: when a negation syntactically attaches to the matrix predicate, it can semantically attach to the embedded predicate. This paper presents an account of NR predicates within Tree Adjoining Grammar (TAG). We propose a lexical semantic interpretation that heavily relies on a Montague-like semantics for TAG and on higher-order types

    CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

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    One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language. Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions. There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization. The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator. As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.) Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace. We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent

    CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

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    This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

    The Automatic Acquisition of Knowledge about Discourse Connectives

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    Institute for Communicating and Collaborative SystemsThis thesis considers the automatic acquisition of knowledge about discourse connectives. It focuses in particular on their semantic properties, and on the relationships that hold between them. There is a considerable body of theoretical and empirical work on discourse connectives. For example, Knott (1996) motivates a taxonomy of discourse connectives based on relationships between them, such as HYPONYMY and EXCLUSIVE, which are defined in terms of substitution tests. Such work requires either great theoretical insight or manual analysis of large quantities of data. As a result, to date no manual classification of English discourse connectives has achieved complete coverage. For example, Knott gives relationships between only about 18% of pairs obtained from a list of 350 discourse connectives. This thesis explores the possibility of classifying discourse connectives automatically, based on their distributions in texts. This thesis demonstrates that state-of-the-art techniques in lexical acquisition can successfully be applied to acquiring information about discourse connectives. Central to this thesis is the hypothesis that distributional similarity correlates positively with semantic similarity. Support for this hypothesis has previously been found for word classes such as nouns and verbs (Miller and Charles, 1991; Resnik and Diab, 2000, for example), but there has been little exploration of the degree to which it also holds for discourse connectives. We investigate the hypothesis through a number of machine learning experiments. These experiments all use unsupervised learning techniques, in the sense that they do not require any manually annotated data, although they do make use of an automatic parser. First, we show that a range of semantic properties of discourse connectives, such as polarity and veridicality (whether or not the semantics of a connective involves some underlying negation, and whether the connective implies the truth of its arguments, respectively), can be acquired automatically with a high degree of accuracy. Second, we consider the tasks of predicting the similarity and substitutability of pairs of discourse connectives. To assist in this, we introduce a novel information theoretic function based on variance that, in combination with distributional similarity, is useful for learning such relationships. Third, we attempt to automatically construct taxonomies of discourse connectives capturing substitutability relationships. We introduce a probability model of taxonomies, and show that this can improve accuracy on learning substitutability relationships. Finally, we develop an algorithm for automatically constructing or extending such taxonomies which uses beam search to help find the optimal taxonomy

    Wide-coverage statistical parsing with minimalist grammars

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    Syntactic parsing is the process of automatically assigning a structure to a string of words, and is arguably a necessary prerequisite for obtaining a detailed and precise representation of sentence meaning. For many NLP tasks, it is sufficient to use parsers based on simple context free grammars. However, for tasks in which precision on certain relatively rare but semantically crucial constructions (such as unbounded wh-movements for open domain question answering) is important, more expressive grammatical frameworks still have an important role to play. One grammatical framework which has been conspicuously absent from journals and conferences on Natural Language Processing (NLP), despite continuing to dominate much of theoretical syntax, is Minimalism, the latest incarnation of the Transformational Grammar (TG) approach to linguistic theory developed very extensively by Noam Chomsky and many others since the early 1950s. Until now, all parsers using genuine transformational movement operations have had only narrow coverage by modern standards, owing to the lack of any wide-coverage TG grammars or treebanks on which to train statistical models. The received wisdom within NLP is that TG is too complex and insufficiently formalised to be applied to realistic parsing tasks. This situation is unfortunate, as it is arguably the most extensively developed syntactic theory across the greatest number of languages, many of which are otherwise under-resourced, and yet the vast majority of its insights never find their way into NLP systems. Conversely, the process of constructing large grammar fragments can have a salutary impact on the theory itself, forcing choices between competing analyses of the same construction, and exposing incompatibilities between analyses of different constructions, along with areas of over- and undergeneration which may otherwise go unnoticed. This dissertation builds on research into computational Minimalism pioneered by Ed Stabler and others since the late 1990s to present the first ever wide-coverage Minimalist Grammar (MG) parser, along with some promising initial experimental results. A wide-coverage parser must of course be equipped with a wide-coverage grammar, and this dissertation will therefore also present the first ever wide-coverage MG, which has analyses with a high level of cross-linguistic descriptive adequacy for a great many English constructions, many of which are taken or adapted from proposals in the mainstream Minimalist literature. The grammar is very deep, in the sense that it describes many long-range dependencies which even most other expressive wide-coverage grammars ignore. At the same time, it has also been engineered to be highly constrained, with continuous computational testing being applied to minimize both under- and over-generation. Natural language is highly ambiguous, both locally and globally, and even with a very strong formal grammar, there may still be a great many possible structures for a given sentence and its substrings. The standard approach to resolving such ambiguity is to equip the parser with a probability model allowing it to disregard certain unlikely search paths, thereby increasing both its efficiency and accuracy. The most successful parsing models are those extracted in a supervised fashion from labelled data in the form of a corpus of syntactic trees, known as a treebank. Constructing such a treebank from scratch for a different formalism is extremely time-consuming and expensive, however, and so the standard approach is to map the trees in an existing treebank into trees of the target formalism. Minimalist trees are considerably more complex than those of other formalisms, however, containing many more null heads and movement operations, making this conversion process far from trivial. This dissertation will describe a method which has so far been used to convert 56% of the Penn Treebank trees into MG trees. Although still under development, the resulting MGbank corpus has already been used to train a statistical A* MG parser, described here, which has an expected asymptotic time complexity of O(n3); this is much better than even the most optimistic worst case analysis for the formalism

    Unsupervised extraction of semantic relations using discourse information

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    La compréhension du langage naturel repose souvent sur des raisonnements de sens commun, pour lesquels la connaissance de relations sémantiques, en particulier entre prédicats verbaux, peut être nécessaire. Cette thèse porte sur la problématique de l'utilisation d'une méthode distributionnelle pour extraire automatiquement les informations sémantiques nécessaires à ces inférences de sens commun. Des associations typiques entre des paires de prédicats et un ensemble de relations sémantiques (causales, temporelles, de similarité, d'opposition, partie/tout) sont extraites de grands corpus, par l'exploitation de la présence de connecteurs du discours signalant typiquement ces relations. Afin d'apprécier ces associations, nous proposons plusieurs mesures de signifiance inspirées de la littérature ainsi qu'une mesure novatrice conçue spécifiquement pour évaluer la force du lien entre les deux prédicats et la relation. La pertinence de ces mesures est évaluée par le calcul de leur corrélation avec des jugements humains, obtenus par l'annotation d'un échantillon de paires de verbes en contexte discursif. L'application de cette méthodologie sur des corpus de langue française et anglaise permet la construction d'une ressource disponible librement, Lecsie (Linked Events Collection for Semantic Information Extraction). Celle-ci est constituée de triplets: des paires de prédicats associés à une relation; à chaque triplet correspondent des scores de signifiance obtenus par nos mesures.Cette ressource permet de dériver des représentations vectorielles de paires de prédicats qui peuvent être utilisées comme traits lexico-sémantiques pour la construction de modèles pour des applications externes. Nous évaluons le potentiel de ces représentations pour plusieurs applications. Concernant l'analyse du discours, les tâches de la prédiction d'attachement entre unités du discours, ainsi que la prédiction des relations discursives spécifiques les reliant, sont explorées. En utilisant uniquement les traits provenant de notre ressource, nous obtenons des améliorations significatives pour les deux tâches, par rapport à plusieurs bases de référence, notamment des modèles utilisant d'autres types de représentations lexico-sémantiques. Nous proposons également de définir des ensembles optimaux de connecteurs mieux adaptés à des applications sur de grands corpus, en opérant une réduction de dimension dans l'espace des connecteurs, au lieu d'utiliser des groupes de connecteurs composés manuellement et correspondant à des relations prédéfinies. Une autre application prometteuse explorée dans cette thèse concerne les relations entre cadres sémantiques (semantic frames, e.g. FrameNet): la ressource peut être utilisée pour enrichir cette structure par des relations potentielles entre frames verbaux à partir des associations entre leurs verbes. Ces applications diverses démontrent les contributions prometteuses amenées par notre approche permettant l'extraction non supervisée de relations sémantiques.Natural language understanding often relies on common-sense reasoning, for which knowledge about semantic relations, especially between verbal predicates, may be required. This thesis addresses the challenge of using a distibutional method to automatically extract the necessary semantic information for common-sense inference. Typical associations between pairs of predicates and a targeted set of semantic relations (causal, temporal, similarity, opposition, part/whole) are extracted from large corpora, by exploiting the presence of discourse connectives which typically signal these semantic relations. In order to appraise these associations, we provide several significance measures inspired from the literature as well as a novel measure specifically designed to evaluate the strength of the link between the two predicates and the relation. The relevance of these measures is evaluated by computing their correlations with human judgments, based on a sample of verb pairs annotated in context. The application of this methodology to French and English corpora leads to the construction of a freely available resource, Lecsie (Linked Events Collection for Semantic Information Extraction), which consists of triples: pairs of event predicates associated with a relation; each triple is assigned significance scores based on our measures. From this resource, vector-based representations of pairs of predicates can be induced and used as lexical semantic features to build models for external applications. We assess the potential of these representations for several applications. Regarding discourse analysis, the tasks of predicting attachment of discourse units, as well as predicting the specific discourse relation linking them, are investigated. Using only features from our resource, we obtain significant improvements for both tasks in comparison to several baselines, including ones using other representations of the pairs of predicates. We also propose to define optimal sets of connectives better suited for large corpus applications by performing a dimension reduction in the space of the connectives, instead of using manually composed groups of connectives corresponding to predefined relations. Another promising application pursued in this thesis concerns relations between semantic frames (e.g. FrameNet): the resource can be used to enrich this sparse structure by providing candidate relations between verbal frames, based on associations between their verbs. These diverse applications aim to demonstrate the promising contributions provided by our approach, namely allowing the unsupervised extraction of typed semantic relations

    Superseded: Grammatical theory: From transformational grammar to constraint-based approaches. Second revised and extended edition.

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    This book is superseded by the third edition, available at http://langsci-press.org/catalog/book/255. This book introduces formal grammar theories that play a role in current linguistic theorizing (Phrase Structure Grammar, Transformational Grammar/Government & Binding, Generalized Phrase Structure Grammar, Lexical Functional Grammar, Categorial Grammar, Head-​Driven Phrase Structure Grammar, Construction Grammar, Tree Adjoining Grammar). The key assumptions are explained and it is shown how the respective theory treats arguments and adjuncts, the active/passive alternation, local reorderings, verb placement, and fronting of constituents over long distances. The analyses are explained with German as the object language. The second part of the book compares these approaches with respect to their predictions regarding language acquisition and psycholinguistic plausibility. The nativism hypothesis, which assumes that humans posses genetically determined innate language-specific knowledge, is critically examined and alternative models of language acquisition are discussed. The second part then addresses controversial issues of current theory building such as the question of flat or binary branching structures being more appropriate, the question whether constructions should be treated on the phrasal or the lexical level, and the question whether abstract, non-visible entities should play a role in syntactic analyses. It is shown that the analyses suggested in the respective frameworks are often translatable into each other. The book closes with a chapter showing how properties common to all languages or to certain classes of languages can be captured. The book is a translation of the German book Grammatiktheorie, which was published by Stauffenburg in 2010. The following quotes are taken from reviews: With this critical yet fair reflection on various grammatical theories, Müller fills what was a major gap in the literature. Karen Lehmann, Zeitschrift für Rezen­sio­nen zur ger­man­is­tis­chen Sprach­wis­senschaft, 2012 Stefan Müller’s recent introductory textbook, Gram­matik­the­o­rie, is an astonishingly comprehensive and insightful survey for beginning students of the present state of syntactic theory. Wolfgang Sternefeld und Frank Richter, Zeitschrift für Sprach­wissen­schaft, 2012 This is the kind of work that has been sought after for a while [...] The impartial and objective discussion offered by the author is particularly refreshing. Werner Abraham, Germanistik, 2012   This book is a new edition of http://langsci-press.org/catalog/book/25

    Superseded: Grammatical theory: From transformational grammar to constraint-based approaches. Second revised and extended edition.

    Get PDF
    This book is superseded by the third edition, available at http://langsci-press.org/catalog/book/255. This book introduces formal grammar theories that play a role in current linguistic theorizing (Phrase Structure Grammar, Transformational Grammar/Government & Binding, Generalized Phrase Structure Grammar, Lexical Functional Grammar, Categorial Grammar, Head-​Driven Phrase Structure Grammar, Construction Grammar, Tree Adjoining Grammar). The key assumptions are explained and it is shown how the respective theory treats arguments and adjuncts, the active/passive alternation, local reorderings, verb placement, and fronting of constituents over long distances. The analyses are explained with German as the object language. The second part of the book compares these approaches with respect to their predictions regarding language acquisition and psycholinguistic plausibility. The nativism hypothesis, which assumes that humans posses genetically determined innate language-specific knowledge, is critically examined and alternative models of language acquisition are discussed. The second part then addresses controversial issues of current theory building such as the question of flat or binary branching structures being more appropriate, the question whether constructions should be treated on the phrasal or the lexical level, and the question whether abstract, non-visible entities should play a role in syntactic analyses. It is shown that the analyses suggested in the respective frameworks are often translatable into each other. The book closes with a chapter showing how properties common to all languages or to certain classes of languages can be captured. The book is a translation of the German book Grammatiktheorie, which was published by Stauffenburg in 2010. The following quotes are taken from reviews: With this critical yet fair reflection on various grammatical theories, Müller fills what was a major gap in the literature. Karen Lehmann, Zeitschrift für Rezen­sio­nen zur ger­man­is­tis­chen Sprach­wis­senschaft, 2012 Stefan Müller’s recent introductory textbook, Gram­matik­the­o­rie, is an astonishingly comprehensive and insightful survey for beginning students of the present state of syntactic theory. Wolfgang Sternefeld und Frank Richter, Zeitschrift für Sprach­wissen­schaft, 2012 This is the kind of work that has been sought after for a while [...] The impartial and objective discussion offered by the author is particularly refreshing. Werner Abraham, Germanistik, 2012   This book is a new edition of http://langsci-press.org/catalog/book/25
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