Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all preposition tokens in a 55,000-word corpus of English. In an attempt to resolve problematic cases in English and apply the scheme to adpositions and case markers in other languages, we reconsider the assumption that an adposition’s lexical contribution is equivalent to the role/relation that it mediates, embracing the potential for construal to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition\u27s lexical function, and discuss how it would allow for a simpler inventory of labels

Identifying and modelling polysemous senses of spatial prepositions in referring expressions

In this paper we analyse the issue of reference using spatial language and examine how the polysemy exhibited by spatial prepositions can be incorporated into semantic models for situated dialogue. After providing a brief overview of polysemy in spatial language and a review of related work, we describe an experimental study we used to collect data on a set of relevant spatial prepositions. We then establish a semantic model in which to integrate polysemy (the Baseline Prototype Model), which we test against a Simple Relation Model and a Perceptron Model. To incorporate polysemy into the baseline model we introduce two methods of identifying polysemes in grounded settings. The first is based on ‘ideal meanings’ and a modification of the ‘principled polysemy’ framework and the second is based on ‘object-specific features’. In order to compare polysemes and aid typicality judgements we then introduce a notion of ‘polyseme hierarchy’. Finally, we test the performance of the polysemy models against the Baseline Prototype Model and Perceptron Model and discuss the improvements shown by the polysemy models

Keywords at Work: Investigating Keyword Extraction in Social Media Applications

This dissertation examines a long-standing problem in Natural Language Processing (NLP) -- keyword extraction -- from a new angle. We investigate how keyword extraction can be formulated on social media data, such as emails, product reviews, student discussions, and student statements of purpose. We design novel graph-based features for supervised and unsupervised keyword extraction from emails, and use the resulting system with success to uncover patterns in a new dataset -- student statements of purpose. Furthermore, the system is used with new features on the problem of usage expression extraction from product reviews, where we obtain interesting insights. The system while used on student discussions, uncover new and exciting patterns. While each of the above problems is conceptually distinct, they share two key common elements -- keywords and social data. Social data can be messy, hard-to-interpret, and not easily amenable to existing NLP resources. We show that our system is robust enough in the face of such challenges to discover useful and important patterns. We also show that the problem definition of keyword extraction itself can be expanded to accommodate new and challenging research questions and datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145929/1/lahiri_1.pd