Introduction

Introduction to genericity in the nominal, verbal and sentential domain

Introduction

International audienceThis introductory chapter is organized into three parts. The first part focuses on the syntactic structure and compositional interpretation of determiner phrases, and frames the ontological issues related to reference to kinds in this context. It addresses a series of ontological issues relevant to the analysis of natural language: in order to account for linguistic data, must we postulate the existence of kinds, viewed as a type of entities, distinguished from particulars or tokens? What is the relationship between kinds and sets of entities, between kinds and properties, between kinds and sets of properties? The second part is comprised of three sections which are dedicated respectively to the stage-level/individual-level distinction, to the contribution of unboundedness and plurality, and to the dispositional reading of generic sentences. The questions addressed in this part pertain to the relationship between genericity, habituality, abilities, and dispositions. The third part examines the type of generic sentences, opposing analytic vs synthetic judgments, and raises the question of the notion of normality. It comprises two sections. The first section addresses the issue of the linguistic manifestation of the analytic/synthetic distinction and investigates the sources of the available interpretations for indefinite generic sentences, bare plurals, and definite plural generics. The second section discusses the notion of normality, comparing the view of normality as a statistical fact and the view of normality as a normative one

Seabasing and joint expeditionary logistics

Student Integrated ProjectIncludes supplementary material. Executive Summary and Presentation.Recent conflicts such as Operation Desert Shield/Storm and Operation Iraqi Freedom highlight the logistics difficulties the United States faces by relying on foreign access and infrastructure and large supply stockpiles ashore to support expeditionary operations. The Navy's transformational vision for the future, Sea Power 21, involves Seabasing as a way to address these difficulties by projecting and sustaining joint forces globally from the sea. This study analyzes logistics flow to, within and from a Sea Base to an objective, and the architectures and systems needed to rapidly deploy and sustain a brigade-size force. Utilizing the Joint Capabilities Integration and Development System (JCIDS), this study incorporates a systems engineering framework to examine current systems, programs of record and proposed systems out to the year 2025. Several capability gaps that hamper a brigade-size force from seizing the initiative anywhere in the world within a 10-day period point to a need for dedicated lift assets, such as high-speed surface ships or lighter-than-air ships, to facilitate the rapid formation of the Sea Base. Additionally, the study identifies the need for large-payload/high-speed or load-once/direct-to- objective connector capabilities to minimize the number of at-sea transfers required to employ such a force from the Sea Base in 10 hrs. With these gaps addressed, the Joint Expeditionary Brigade is supportable from the Sea Base.http://archive.org/details/seabasingndjoint109456918N

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

Learning with Joint Inference and Latent Linguistic Structure in Graphical Models

Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application. In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches. Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task

Deep Learning With Sentiment Inference For Discourse-Oriented Opinion Analysis

Opinions are omnipresent in written and spoken text ranging from editorials, reviews, blogs, guides, and informal conversations to written and broadcast news. However, past research in NLP has mainly addressed explicit opinion expressions, ignoring implicit opinions. As a result, research in opinion analysis has plateaued at a somewhat superficial level, providing methods that only recognize what is explicitly said and do not understand what is implied. In this dissertation, we develop machine learning models for two tasks that presumably support propagation of sentiment in discourse, beyond one sentence. The first task we address is opinion role labeling, i.e.\ the task of detecting who expressed a given attitude toward what or who. The second task is abstract anaphora resolution, i.e.\ the task of finding a (typically) non-nominal antecedent of pronouns and noun phrases that refer to abstract objects like facts, events, actions, or situations in the preceding discourse. We propose a neural model for labeling of opinion holders and targets and circumvent the problems that arise from the limited labeled data. In particular, we extend the baseline model with different multi-task learning frameworks. We obtain clear performance improvements using semantic role labeling as the auxiliary task. We conduct a thorough analysis to demonstrate how multi-task learning helps, what has been solved for the task, and what is next. We show that future developments should improve the ability of the models to capture long-range dependencies and consider other auxiliary tasks such as dependency parsing or recognizing textual entailment. We emphasize that future improvements can be measured more reliably if opinion expressions with missing roles are curated and if the evaluation considers all mentions in opinion role coreference chains as well as discontinuous roles. To the best of our knowledge, we propose the first abstract anaphora resolution model that handles the unrestricted phenomenon in a realistic setting. We cast abstract anaphora resolution as the task of learning attributes of the relation that holds between the sentence with the abstract anaphor and its antecedent. We propose a Mention-Ranking siamese-LSTM model (MR-LSTM) for learning what characterizes the mentioned relation in a data-driven fashion. The current resources for abstract anaphora resolution are quite limited. However, we can train our models without conventional data for abstract anaphora resolution. In particular, we can train our models on many instances of antecedent-anaphoric sentence pairs. Such pairs can be automatically extracted from parsed corpora by searching for a common construction which consists of a verb with an embedded sentence (complement or adverbial), applying a simple transformation that replaces the embedded sentence with an abstract anaphor, and using the cut-off embedded sentence as the antecedent. We refer to the extracted data as silver data. We evaluate our MR-LSTM models in a realistic task setup in which models need to rank embedded sentences and verb phrases from the sentence with the anaphor as well as a few preceding sentences. We report the first benchmark results on an abstract anaphora subset of the ARRAU corpus \citep{uryupina_et_al_2016} which presents a greater challenge due to a mixture of nominal and pronominal anaphors as well as a greater range of confounders. We also use two additional evaluation datasets: a subset of the CoNLL-12 shared task dataset \citep{pradhan_et_al_2012} and a subset of the ASN corpus \citep{kolhatkar_et_al_2013_crowdsourcing}. We show that our MR-LSTM models outperform the baselines in all evaluation datasets, except for events in the CoNLL-12 dataset. We conclude that training on the small-scale gold data works well if we encounter the same type of anaphors at the evaluation time. However, the gold training data contains only six shell nouns and events and thus resolution of anaphors in the ARRAU corpus that covers a variety of anaphor types benefits from the silver data. Our MR-LSTM models for resolution of abstract anaphors outperform the prior work for shell noun resolution \citep{kolhatkar_et_al_2013} in their restricted task setup. Finally, we try to get the best out of the gold and silver training data by mixing them. Moreover, we speculate that we could improve the training on a mixture if we: (i) handle artifacts in the silver data with adversarial training and (ii) use multi-task learning to enable our models to make ranking decisions dependent on the type of anaphor. These proposals give us mixed results and hence a robust mixed training strategy remains a challenge

The Restriction on Predicative Codas in Existential There-Clauses. Theoretical and Empirical Perspectives

This thesis investigates the so-called Predicate Restriction (PR) in English existential sentences. The general consensus in the literature is that only stage-level predicates (i.e. predicates denoting temporary properties, like 'sick') can appear in the coda of an existential sentence, while individual-level predicates (i.e. predicates that denote permanent properties, like 'tall') are excluded. After a discussion of the various theoretical approaches to the PR (syntactic, semantic, as well as pragmatic), this thesis pursues two empirical studies, using both corpus data from the BNC and a judgment study. Both studies confirm the theoretical preference for SLP (in a general sense), but also show that the distinction between predicates should not be reduced to a binary SLP–ILP choice. Predicates should rather be analysed in a more fine-grained system with multiple factors, as done in Jäger (2001)

Kopula-Prädikativ-Konstruktionen als Syntax/Semantik-Schnittstellen

Anders als man auf den ersten Blick vermuten könnte ist die vorliegende Sammlung von Aufsätzen nicht aus den Beiträgen eines thematisch einschlägigen Workshops kompiliert worden (ein solcher ist erst für Herbst 1999 vorgesehen), sondern sie entstammt den Diskussionsrunden des Lexikonzirkels am ZAS, die - initiiert vom Projekt "Schnittstellen der Semantik: Kopula-Prädikativ-Konstruktionen" - seit 1997 regelmäßig und mit zunehmender Einbindung externer Mitarbeiter stattgefunden haben. Daß das 1998 mit nur anderthalb DFG-Stellen besetzte Projekt am ZAS eine solche Irradiationswirkung ausübt, verdankt sich wohl dem Zusammentreffen zweier günstiger Bedingungen. Die erste Bedingung liefern die im Konzept des ZAS angelegten Möglichkeiten kooperativer Forschungsförderung, die hier in beherzter Überschreitung administrativer Grenzen erfolgreich umgesetzt werden konnten. Die Beiträge sind eine Zwischenbilanz von Studien, die im ZAS-Projekt selbst betrieben wurden, und von Studien, die - vom Projekt angeregt - nach kurzem so in dessen Forschung verwickelt waren, daß die resultierende Verflechtung zur unverzichtbaren Grundlage der weiteren Arbeit des Projekts geworden ist. Die zweite Bedingung besteht offenkundig in der Problemhaltigkeit des Themas und der daraus resultierenden theoretischen Attraktivität. Was macht Kopula-Prädikativ-Konstruktionen unter dem Blickwinkel ihrer grammatischen Schnittstellen so attraktiv