Dialectometric analysis of language variation in Twitter

In the last few years, microblogging platforms such as Twitter have given rise to a deluge of textual data that can be used for the analysis of informal communication between millions of individuals. In this work, we propose an information-theoretic approach to geographic language variation using a corpus based on Twitter. We test our models with tens of concepts and their associated keywords detected in Spanish tweets geolocated in Spain. We employ dialectometric measures (cosine similarity and Jensen-Shannon divergence) to quantify the linguistic distance on the lexical level between cells created in a uniform grid over the map. This can be done for a single concept or in the general case taking into account an average of the considered variants. The latter permits an analysis of the dialects that naturally emerge from the data. Interestingly, our results reveal the existence of two dialect macrovarieties. The first group includes a region-specific speech spoken in small towns and rural areas whereas the second cluster encompasses cities that tend to use a more uniform variety. Since the results obtained with the two different metrics qualitatively agree, our work suggests that social media corpora can be efficiently used for dialectometric analyses.Comment: 10 pages, 7 figures, 1 table. Accepted to VarDial 201

Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.Comment: in LREC 201

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

Identifying and Modeling Code-Switched Language

Code-switching is the phenomenon by which bilingual speakers switch between multiple languages during written or spoken communication. The importance of developing language technologies that are able to process code-switched language is immense, given the large populations that routinely code-switch. Current NLP and Speech models break down when used on code-switched data, interrupting the language processing pipeline in back-end systems and forcing users to communicate in ways which for them are unnatural. There are four main challenges that arise in building code-switched models: lack of code-switched data on which to train generative language models; lack of multilingual language annotations on code-switched examples which are needed to train supervised models; little understanding of how to leverage monolingual and parallel resources to build better code-switched models; and finally, how to use these models to learn why and when code-switching happens across language pairs. In this thesis, I look into different aspects of these four challenges. The first part of this thesis focuses on how to obtain reliable corpora of code-switched language. We collected a large corpus of code-switched language from social media using a combination of sets of anchor words that exist in one language and sentence-level language taggers. The newly obtained corpus is superior to other corpora collected via different strategies when it comes to the amount and type of bilingualism in it. It also helps train better language tagging models. We also have proposed a new annotation scheme to obtain part-of-speech tags for code-switched English-Spanish language. The annotation scheme is composed of three different subtasks including automatic labeling, word-specific questions labeling and question-tree word labeling. The part-of-speech labels obtained for the Miami Bangor corpus of English-Spanish conversational speech show very high agreement and accuracy. The second section of this thesis focuses on the tasks of part-of-speech tagging and language modeling. For the first task, we proposed a state-of-the-art approach to part-of-speech tagging of code-switched English-Spanish data based on recurrent neural networks.Our models were tested on the Miami Bangor corpus on the task of POS tagging alone, for which we achieved 96.34% accuracy, and joint part-of-speech and language ID tagging,which achieved similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). For the task of language modeling, we first conducted an exhaustive analysis of the relationship between cognate words and code-switching. We then proposed a set of cognate-based features that helped improve language modeling performance by 12% relative points. Furthermore, we showed that these features can also be used across language pairs and still obtain performance improvements. Finally, we tackled the question of how to use monolingual resources for code-switching models by pre-training state-of-the-art cross-lingual language models on large monolingual corpora and fine-tuning them on the tasks of language modeling and word-level language tagging on code-switched data. We obtained state-of-the-art results on both tasks

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one