Domain adaptation arises in supervised learning when the training (source domain) and test (target domain) data have different distributions. Let X and Y denote the features and target, respectively, previous work on domain adaptation mainly considers the covariate shift situation where the distribution of the features P(X) changes across domains while the conditional distribution P(Y∣X) stays ...

Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space. It has been widely used in recent years to boost the performance of a variety of NLP tasks such as Named Entity Recognition, Syntactic Parsing and Sentiment Analysis. Classic word embedding methods such as Word2Vec and GloVe work well ...

Abstract This article describes the research design and findings from a use-inspired study of online text-based mathematics resources. We sought to understand whether how existing interest, together with learner characteristics prior coursework in proof scheme, influenced comprehension mathematical argumentation triggered interest two types text: (1) text featuring concrete, real-world applicat...

This thesis is focused on building knowledge bases (KBs) for scientific domains. Specifically, we create structured representations of technical-domain information using unsupervised or semi supervised learning methods. This work is inspired by recent advances in knowledge base construction based on Web text. However, in the technical domains we consider here, we have grounded data about the ob...

We present the results of a coarse-grained sense annotation task on verbs, nouns and adjectives across six textual domains in Danish. We present the domain-wise differences in intercoder agreement and discuss how the applicability and validity of the sense inventory vary depending on domain. We find that domain-wise agreement is not higher in very canonical or edited text. In fact, newswire tex...

This paper describes a system that learns discourse rules for domain-speci c analysis of unrestricted text. The goal of discourse analysis in this context is to transform locally identi ed references to relevant information in the text into a coherent representation of the entire text. This involves a complex series of decisions about merging coreferential objects, ltering out irrelevant inform...

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract ...

Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of...

This paper describes an ongoing project which has the goal of improving machine translation quality by increasing knowledge about the text to be translated. A basic piece of such knowledge is the domain or subject field of the text. When this is known, it is possible to improve meaning selection appropriate to that domain. Our current effort consists in automating both recognition of the text’s...

The Enron Email Corpus provides “Real World” text in the business email domain, which is a target domain for many speech and language applications. We present a section of this corpus annotated with number senses labelling each number as a date, time, year, telephone number etc. We show that sense categories and their frequencies are very different in this domain than in newswire text. The anno...