Transfer Learning for Low-Resource Sentiment Analysis

Sentiment analysis is the process of identifying and extracting subjective information from text. Despite the advances to employ cross-lingual approaches in an automatic way, the implementation and evaluation of sentiment analysis systems require language-specific data to consider various sociocultural and linguistic peculiarities. In this paper, the collection and annotation of a dataset are described for sentiment analysis of Central Kurdish. We explore a few classical machine learning and neural network-based techniques for this task. Additionally, we employ an approach in transfer learning to leverage pretrained models for data augmentation. We demonstrate that data augmentation achieves a high F$_1$ score and accuracy despite the difficulty of the task.Comment: 14 pages - under review at ACM TALLI

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe

Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009

Linguistic annotation in/for corpus linguistics

This article surveys linguistic annotation in corpora and corpus linguistics. We first define the concept of 'corpus ' as a radial category and then, in Section 2, discuss a variety of kinds of information for which corpora are annotated and that are exploited in contemporary corpus linguistics. Section 3 then exemplifies many current formats of annotation with an eye to highlighting both the diversity of formats currently available and the emergence of XML annotation as, for now, the most widespread form of annotation. Section 4 summarizes and concludes with desiderata for future developments.

Corpus-based typology: Applications, challenges and some solutions

Over the last few years, the number of corpora that can be used for language comparison has dramatically increased. The corpora are so diverse in their structure, size and annotation style, that a novice might not know where to start. The present paper charts this new and changing territory, providing a few landmarks, warning signs and safe paths. Although no corpora corpus at present can replace the traditional type of typological data based on language description in reference grammars, they corpora can help with diverse tasks, being particularly well suited for investigating probabilistic and gradient properties of languages and for discovering and interpreting cross-linguistic generalizations based on processing and communicative mechanisms. At the same time, the use of corpora for typological purposes has not only advantages and opportunities, but also numerous challenges. This paper also contains an empirical case study addressing two pertinent problems: the role of text types in language comparison and the problem of the word as a comparative concept

Cross-Lingual Link Discovery for Under-Resourced Languages

CC BY-NC 4.0In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We first introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We define under-resourced languages with a specific focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources

Corpus linguistics and language learning: bootstrapping linguistic knowledge and resources from text

This submission for the award of the degree of PhD by published work must: “make a contribution to knowledge in a coherent and related subject area; demonstrate originality and independent critical ability; satisfy the examiners that it is of sufficient merit to qualify for the award of the degree of PhD.” It includes a selection of my work as a Lecturer (and later, Senior Lecturer) at Leeds University, from 1984 to the present. The overall theme of my research has been bootstrapping linguistic knowledge and resources from text. A persistent strand of interest has been unsupervised and semi-supervised machine learning of linguistic knowledge from textual sources; the attraction of this approach is that I could start with English, but go on to apply analogous techniques to other languages, in particular Arabic. This theme covers a broad range of research over more than 20 years at Leeds University which I have divided into 8 sub-topics: A: Constituent-Likelihood statistical modelling of English grammar; B: Machine Learning of grammatical patterns from a corpus; C: Detecting grammatical errors in English text; D: Evaluation of English grammatical annotation models; E: Machine Learning of semantic language models; F: Applications in English language teaching; G: Arabic corpus linguistics; H: Applications in Computing teaching and research. The first section builds on my early years as a lecturer at Leeds University, when my research was essentially a progression from my previous work at Lancaster University on the LOB Corpus Part-of-Speech Tagging project (which resulted in the Tagged LOB Corpus, a resource for Corpus Linguistics research still in use today); I investigated a range of ideas for extending and/or applying techniques related to Part-of-Speech tagging in Corpus Linguistics. The second section covers a range of co-authored papers representing grant-funded research projects in Corpus Linguistics; in this mode of research, I had to come up with the original ideas and guide the project, but much of the detailed implementation was down to research assistant staff. Another highly productive mode of research has been supervision of research students, leading to further jointly-authored research papers. I helped formulate the research plans, and guided and advised the students; as with research-grant projects, the detailed implementation of the research has been down to the research students. The third section includes a few of the most significant of these jointly-authored Corpus Linguistics research papers. A “standard” PhD generally includes a survey of the field to put the work in context; so as a fourth section, I include some survey papers aimed at introducing new developments in corpus linguistics to a wider audience