Investigating an open methodology for designing domain-specific language collections

With this research and design paper, we are proposing that Open Educational Resources (OERs) and Open Access (OA) publications give increasing access to high quality online educational and research content for the development of powerful domain-specific language collections that can be further enhanced linguistically with the Flexible Language Acquisition System (FLAX, http://flax.nzdl.org). FLAX uses the Greenstone digital library system, which is a widely used open-source software that enables end users to build collections of documents and metadata directly onto the Web (Witten, Bainbridge, & Nichols, 2010). FLAX offers a powerful suite of interactive text-mining tools, using Natural Language Processing and Artificial Intelligence designs, to enable novice collections builders to link selected language content to large pre-processed linguistic databases. An open methodology trialed at Queen Mary University of London in collaboration with the OER Research Hub at the UK Open University demonstrates how applying open corpus-based designs and technologies can enhance open educational practices among language teachers and subject academics for the preparation and delivery of courses in English for Specific Academic Purposes (ESAP)

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance

Second language learning in the context of MOOCs

Massive Open Online Courses are becoming popular educational vehicles through which universities reach out to non-traditional audiences. Many enrolees hail from other countries and cultures, and struggle to cope with the English language in which these courses are invariably offered. Moreover, most such learners have a strong desire and motivation to extend their knowledge of academic English, particularly in the specific area addressed by the course. Online courses provide a compelling opportunity for domain-specific language learning. They supply a large corpus of interesting linguistic material relevant to a particular area, including supplementary images (slides), audio and video. We contend that this corpus can be automatically analysed, enriched, and transformed into a resource that learners can browse and query in order to extend their ability to understand the language used, and help them express themselves more fluently and eloquently in that domain. To illustrate this idea, an existing online corpus-based language learning tool (FLAX) is applied to a Coursera MOOC entitled Virology 1: How Viruses Work, offered by Columbia University

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems