Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

Linguistics and LIS: A Research Agenda

Linguistics and Library and Information Science (LIS) are both interdisciplinary fields that draws from areas such as languages, psychology, sociology, cognitive science, computer science, anthropology, education, and management. The theories and methods of linguistic research can have significant explanatory power for LIS. This article presents a research agenda for LIS that proposes the use of linguistic analysis methods, including discourse analysis, typology, and genre theory

Text reconstruction activities and teaching language forms

Even though there is a broad consensus that teaching language forms is facilitative or even necessary in some contexts, there are still disagreements concerning, among other things, how formal aspects of the target language should be taught. One important area of controversy is whether pedagogic intervention should be input-oriented, emphasizing comprehension of the form- meaning mappings represented by specific linguistic features or output-based, requiring learners to produce these features accurately in gradually more communicative activities. The present paper focuses on the latter of these two options and, basing on the claims of Swain‘s (1985, 1995) output hypothesis, it aims to demonstrates how text-reconstruction activities in which learners collaboratively produce written output trigger noticing, hypothesis-testing and metalinguistic reflection on language use. It presents a psycholinguistic and sociolinguistic rationale for the use of such tasks, discusses the types of such activities, provides an overview of research projects investigating their application and, finally, offers a set of implications for classroom use as well as suggestions for further research in this area

The interaction of knowledge sources in word sense disambiguation

Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems