The PAIS? Corpus of Italian Web Texts

PAISA\u27 is a Creative Commons licensed, large web corpus of contemporary Italian. We describe the design, harvesting, and processing steps involved in its creation

PARSEME-It: an Italian corpus annotated with verbal multiword expressions

The paper describes the PARSEME-It corpus, developed within the PARSEME-It project which aims at the development of methods, tools and resources for multiword expressions (MWE) processing for the Italian language. The project is a spin-off of a larger multilingual project for more than 20 languages from several language families, namely the PARSEME COST Action. The first phase of the project was devoted to verbal multiword expressions (VMWEs). They are a particularly interesting lexical phenomenon because of frequent discontinuity and long-distance dependency. Besides they are very challenging for deep parsing and other Natural Language Processing (NLP) tasks. Notably, MWEs are pervasive in natural languages but are particularly difficult to be handled by NLP tools because of their characteristics and idiomaticity. They pose many challenges to their correct identification and processing: they are a linguistic phenomenon on the edge between lexicon and grammar, their meaning is not simply the addition of the meanings of the single constituents of the MWEs and they are ambiguous since in several cases their reading can be literal or idiomatic. Although several studies have been devoted to this topic, to the best of our knowledge, our study is the first attempt to provide a general framework for the identification of VMWEs in running texts and a comprehensive corpus for the Italian language

Deep Learning of Inflection and the Cell-Filling Problem

Machine learning offers two basic strategies for morphology induction: lexical segmentation and surface word relation. The first approach assumes that words can be segmented into morphemes. Inferring a novel inflected form requires identification of morphemic constituents and a strategy for their recombination. The second approach dispenses with segmentation: lexical representations form part of a network of associatively related inflected forms. Production of a novel form consists in filling in one empty node in the network. Here, we present the results of a task of word inflection by a recurrent LSTM network that learns to fill in paradigm cells of incomplete verb paradigms. Although the task does not require morpheme segmentation, we show that accuracy in carrying out the inflection task is a function of the model’s sensitivity to paradigm distribution and morphological structure

Border crossing and trespassing? : Expanding digital humanities research to developing peripheries with the novel digital technologies

Peer reviewe

Wanca in Korp : Text corpora for underresourced Uralic languages

Peer reviewe

Incrementally resolving references in order to identify visually present objects in a situated dialogue setting

Kennington C. Incrementally resolving references in order to identify visually present objects in a situated dialogue setting. Bielefeld: Universität Bielefeld; 2016.The primary concern of this thesis is to model the resolution of spoken referring expressions made in order to identify objects; in particular, everyday objects that can be perceived visually and distinctly from other objects. The practical goal of such a model is for it to be implemented as a component for use in a live, interactive, autonomous spoken dialogue system. The requirement of interaction imposes an added complication; one that has been ignored in previous models and approaches to automatic reference resolution: the model must attempt to resolve the reference incrementally as it unfolds–not wait until the end of the referring expression to begin the resolution process. Beyond components in dialogue systems, reference has been a major player in the philosophy of meaning for longer than a century. For example, Gottlob Frege (1892) has distinguished between Sinn (sense) and Bedeutung (reference), discussed how they are related and how they relate to the meaning of words and expressions. It has furthermore been argued (e.g., Dahlgren (1976)) that reference to entities in the actual world is not just a fundamental notion of semantic theory, but the fundamental notion; for an individual acquiring a language, understanding the meaning of many words and concepts is done via the task of reference, beginning in early childhood. In this thesis, we pursue an account of word meaning that is based on perception of objects; for example, the meaning of the word red is based on visual features that are selected as distinguishing red objects from non-red ones. This thesis proposes two statistical models of incremental reference resolution. Given ex- amples of referring expressions and visual aspects of the objects to which those expressions referred, both model components learn a functional mapping between the words of the refer- ring expressions and the visual aspects. A generative model, the simple incremental update model, presented in Chapter 5, uses a mediating variable to learn the mapping, whereas a dis- criminative model, the words-as-classifiers model, presented in Chapter 6, learns the mapping directly and improves over the generative model. Both models have been evaluated in various reference resolution tasks to objects in virtual scenes as well as real, tangible objects. This thesis shows that both models work robustly and are able to resolve referring expressions made in reference to visually present objects despite realistic, noisy conditions of speech and object recognition. A theoretical and practical comparison is also provided. Special emphasis is given to the discriminative model in this thesis because of its simplicity and ability to represent word meanings. It is in the learning and application of this model that gives credence to the above claim that reference is the fundamental notion for semantic theory and that meanings of (visual) words is done through experiencing referring expressions made to objects that are visually perceivable

CLARIN. The infrastructure for language resources

CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

Computational approaches to semantic change (Volume 6)

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans