Memory-Based Lexical Acquisition and Processing

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page

Multi-facet classification of e-mails in a helpdesk scenario

Helpdesks have to manage a huge amount of support requests which are usually submitted via e-mail. In order to be assigned to experts e ciently, incoming e-mails have to be classi- ed w. r. t. several facets, in particular topic, support type and priority. It is desirable to perform these classi cations automatically. We report on experiments using Support Vector Machines and k-Nearest-Neighbours, respectively, for the given multi-facet classi - cation task. The challenge is to de ne suitable features for each facet. Our results suggest that improvements can be gained for all facets, and they also reveal which features are promising for a particular facet

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field