The manner and time course of updating quantifier scope representations in discourse

We present the results of two experiments, an eye-tracking study and a follow-up self-paced reading study, investigating the interpretation of quantifier scope in sentences with three quantifiers: two indefinites in subject and object positions and a universal distributive quantifier in adjunct position. In addition to the fact that such three-way scope interactions have not been experimentally investigated before, they enable us to distinguish between different theories of quantifier scope interpretation in ways that are not possible when only simpler, two-way interactions are considered. The experiments show that contrary to underspecification theories of scope, a totally ordered scope-hierarchy representation is maintained and modified across sentences and this scope representation cannot be reduced to the truth-conditional/mental model representation of sentential meaning. The experiments also show that the processor uses scope-disambiguating information as early as possible to (re)analyze scope representation

Name Variants for Improving Entity Discovery and Linking

Identifying all names that refer to a particular set of named entities is a challenging task, as quite often we need to consider many features that include a lot of variation like abbreviations, aliases, hypocorism, multilingualism or partial matches. Each entity type can also have specific rules for name variances: people names can include titles, country and branch names are sometimes removed from organization names, while locations are often plagued by the issue of nested entities. The lack of a clear strategy for collecting, processing and computing name variants significantly lowers the recall of tasks such as Named Entity Linking and Knowledge Base Population since name variances are frequently used in all kind of textual content. This paper proposes several strategies to address these issues. Recall can be improved by combining knowledge repositories and by computing additional variances based on algorithmic approaches. Heuristics and machine learning methods then analyze the generated name variances and mark ambiguous names to increase precision. An extensive evaluation demonstrates the effects of integrating these methods into a new Named Entity Linking framework and confirms that systematically considering name variances yields significant performance improvements

Sentence-internal same and its quantificational licensors: A new window into the processing of inverse scope

This paper investigates the processing of sentence-internal "same" with four licensors ("all", "each", "every" and "the") in two orders: licensor+"same (surface scope) and "same"+licensor (inverse scope). Our two self-paced reading studies show that there is no general effect of surface vs. inverse scope, which we take as an argument for a model-oriented view of the processing cost of inverse scope: the inverse scope of quantifiers seems to be costly because of model structure reanalysis, not because of covert scope operations. The second result is methodological: the psycholinguistic investigation of semantic phenomena like the interaction of quantifiers and sentence-internal readings should always involve a context that prompts a deep enough processing of the target expressions. In one of our two studies, participants read the target sentences after reading a scenario introducing the two sets of entities the quantifier NP and the same NP referred to and they were asked to determine whether the sentence was true or false relative to the background scenario every time. In the other study, the participants read the same sentences without any context and there were fewer follow-up comprehension questions. The relevant effects observed in the study with contexts completely disappeared in the out-of-context study, although the participants in both studies were monitored for their level of attention to the experimental task. http://dx.doi.org/10.3765/sp.8.1 BibTeX info</a

Varieties of Distributivity: 'One by One' vs 'Each'

No abstract

The online interpretation of sentence-internal same and distributivity

This paper investigates the processing of sentence-internal same with three licensors (each, all and the) in two orders: licensor+same (surface scope) and same+licensor (inverse scope). Our study shows that (i) there is no effect of surface vs. inverse scope, which we take as an argument for a model-oriented view of the processing cost of inverse scope, and (ii) all is processed faster than each and the with same, which we take as an argument for a particular semantics of distributive licensors

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019

Introducing Orbis: An Extendable Evaluation Pipeline for Named Entity Linking Drill-Down Analysis

Most current evaluation tools are focused solely on benchmarking and comparative evaluations thus only provide aggregated statistics such as precision, recall and F1-measure to assess overall system performance. They do not offer comprehensive analyses up to the level of individual annotations. This paper introduces Orbis, an extendable evaluation pipeline framework developed to allow visual drill-down analyses of individual entities, computed by annotation services, in the context of the text they appear in, in reference to the entities specified in the gold standard

The Role of Visual Rhetoric in Semantic Multimedia: Strategies for Decision Making in Times of Crisis

As semantic multimedia is approaching mainstream, even the great improvements that can be seen in its classic schools, like the data mining inspired Information Retrieval based on metadata analysis, or Computer Vision, might not be enough. We identify a new group that gains traction in the semantic multimedia community and which uses as starting point developments from psychology and visual communication. For the purposes of this article we restrict our domain to visual rhetoric as we consider it to yield the biggest potential for future developments. Living in times when the periods between crises seem to be shorter and shorter, we look at how developments in semantic multimedia can be used for predicting and overcoming crises. We analyze at least 2 aspects related to this: using information visualization to understand the evolution of crises and creating multi-layered semantic multimedia technologies that can easily be adapted to use a variety of sources and solve problems from different domains. In both cases we show how techniques inspired by visual rhetoric (information linking, framing, composition) in conjunction with named entity recognition offer a lot of benefits. The section related to multi-layered semantic multimedia technologies also draws on the lessons learned while designing a prototype application aimed at improving tourism decision making process. The article ends with a discussion on evaluation methods for multi-layered semantic technologies applications. We look at how to evaluate them on both levels: mechanisms (information linking versus raw named entity recognition when generating visuals, for example), and decision making strategies (Do such systems actually solve real problems related to crises, create jobs or at least can they be repurposed to solve other problems than the one with which we have started?)

Crossing the appositive / at-issue meaning boundary

Our goal is to provide systematic evidence from anaphora, presupposition and ellipsis that appositive meaning and at-issue meaning, e.g. as contributed by the relative appositive and the main clause in John, who nearly killed a woman with his car, visited HER in the hospital, have to be integrated into a single, incrementally evolving semantic representation. While previous literature has provided partial arguments to this effect (Nouwen 2007 for anaphora, Amaral et al 2007 and Potts 2009 for both anaphora and presupposition), the systematic nature of this evidence -- in particular, the evidence from ellipsis we will introduce -- has been previously unnoticed. We propose an analysis of these phenomena that integrates the dynamic account of anaphora and ellipsis as discourse reference to individuals and properties (respectively) with an account of at-issue meaning as a proposed update of the input Context Set (CS) that is to be negotiated and of appositive meaning as an actual / imposed update of the CS that is not up for negotiation