18 research outputs found

    Creación de corpus de palabras embebidas de tweets generados en Argentina

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    El procesamiento de textos de cualquier índole es una tarea de gran interés en la comunidad científica. Una de las redes sociales donde frecuentemente las personas se expresan libremente es Twitter, y por lo tanto, es una de las principales fuentes para obtener datos textuales. Para poder realizar cualquier tipo de análisis, como primer paso se debe representar los textos de manera adecuada para que, luego, puedan ser usados por un algoritmo. En este artículo se describe la creación de un corpus de representaciones de palabras obtenidas de Twitter, utilizando Word2Vec. Si bien los conjuntos de tweets utilizados no son masivos, se consideran suficientes para dar el primer paso en la creación de un corpus. Un aporte importante de este trabajo es el entrenamiento de un modelo que captura los modismos y expresiones coloquiales de Argentina, y que incluye emojis y hashtags dentro del espacio vectorial.Text processing of any kind is a task of great interest in the scientific community. One of the social networks where people frequently express themselves freely is Twitter, and therefore, it is one of the main sources for obtaining textual data. In order to perform any type of analysis, the first step is to represent the texts in a suitable way so that they can then be used by an algorithm. This paper describes the creation of a corpus of word representations obtained from Twitter using Word2Vec. Although the sets of tweets used are not massive, they are considered sufficient to take the first step in the creation of a corpus. An important contribution of this work is the training of a model that captures the idioms and colloquial expressions of Argentina, and includes emojis and hashtags within the vector space

    Using Multilingual Word Embeddings for Similarity-Based Word Alignments in a Zero-Shot Setting: Tested on the Case of German–Romansh

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    Using multilingual word embeddings for computing word alignments has been shown to be competetive with statistical word alignment methods. However, the languages on which the experiments were made on were all “seen” languages, i.e., they were part of the training data for the embeddings. In this thesis I show that multilingual word embeddings taken from mBERT can be used for computing word alignments for the “unseen” language Romansh, aligned against German. The performance is on par with a baseline statistical model (fast_align). I also describe the creation of a gold standard for evaluating the quality of word alignments for German–Romansh, as well as the process of data collection for compiling a trilingual corpus containing press releases in German, Italian and Romansh, published by the Swiss Canton of Grisons. From this corpus, I extracted around 80,000 unique sentence pairs for each language combination

    Mono- and cross-lingual paraphrased text reuse and extrinsic plagiarism detection

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    Text reuse is the act of borrowing text (either verbatim or paraphrased) from an earlier written text. It could occur within the same language (mono-lingual) or across languages (cross-lingual) where the reused text is in a different language than the original text. Text reuse and its related problem, plagiarism (the unacknowledged reuse of text), are becoming serious issues in many fields and research shows that paraphrased and especially the cross-lingual cases of reuse are much harder to detect. Moreover, the recent rise in readily available multi-lingual content on the Web and social media has increased the problem to an unprecedented scale. To develop, compare, and evaluate automatic methods for mono- and crosslingual text reuse and extrinsic (finding portion(s) of text that is reused from the original text) plagiarism detection, standard evaluation resources are of utmost importance. However, previous efforts on developing such resources have mostly focused on English and some other languages. On the other hand, the Urdu language, which is widely spoken and has a large digital footprint, lacks resources in terms of core language processing tools and corpora. With this consideration in mind, this PhD research focuses on developing standard evaluation corpora, methods, and supporting resources to automatically detect mono-lingual (Urdu) and cross-lingual (English-Urdu) cases of text reuse and extrinsic plagiarism This thesis contributes a mono-lingual (Urdu) text reuse corpus (COUNTER Corpus) that contains real cases of Urdu text reuse at document-level. Another contribution is the development of a mono-lingual (Urdu) extrinsic plagiarism corpus (UPPC Corpus) that contains simulated cases of Urdu paraphrase plagiarism. Evaluation results, by applying a wide range of state-of-the-art mono-lingual methods on both corpora, shows that it is easier to detect verbatim cases than paraphrased ones. Moreover, the performance of these methods decreases considerably on real cases of reuse. A couple of supporting resources are also created to assist methods used in the cross-lingual (English-Urdu) text reuse detection. A large-scale multi-domain English-Urdu parallel corpus (EUPC-20) that contains parallel sentences is mined from the Web and several bi-lingual (English-Urdu) dictionaries are compiled using multiple approaches from different sources. Another major contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus (TREU Corpus). It contains English to Urdu real cases of text reuse at the document-level. A diversified range of methods are applied on the TREU Corpus to evaluate its usefulness and to show how it can be utilised in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse. A new cross-lingual method is also proposed that uses bilingual word embeddings to estimate the degree of overlap amongst text documents by computing the maximum weighted cosine similarity between word pairs. The overall low evaluation results indicate that it is a challenging task to detect crosslingual real cases of text reuse, especially when the language pairs have unrelated scripts, i.e., English-Urdu. However, an improvement in the result is observed using a combination of methods used in the experiments. The research work undertaken in this PhD thesis contributes corpora, methods, and supporting resources for the mono- and cross-lingual text reuse and extrinsic plagiarism for a significantly under-resourced Urdu and English-Urdu language pair. It highlights that paraphrased and cross-lingual cross-script real cases of text reuse are harder to detect and are still an open issue. Moreover, it emphasises the need to develop standard evaluation and supporting resources for under-resourced languages to facilitate research in these languages. The resources that have been developed and methods proposed could serve as a framework for future research in other languages and language pairs

    Promoting user engagement and learning in search tasks by effective document representation

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    Much research in information retrieval (IR) focuses on optimisation of the rank of relevant retrieval results for single shot ad hoc IR tasks. Relatively little research has been carried out on supporting and promoting user engagement within search tasks. We seek to improve user experience by use of enhanced document snippets to be presented during the search process to promote user engagement with retrieved information. The primary role of document snippets within search has traditionally been to indicate the potential relevance of retrieved items to the user’s information need. Beyond the relevance of an item, it is generally not possible to infer the contents of individual ranked results just by reading the current snippets. We hypothesise that the creation of richer document snippets and summaries, and effective presentation of this information to users will promote effective search and greater user engagement, and support emerging areas such as learning through search. We generate document summaries for a given query by extracting top relevant sentences from retrieved documents. Creation of these summaries goes beyond exist- ing snippet creation methods by comparing content between documents to take into account novelty when selecting content for inclusion in individual document sum- maries. Further, we investigate the readability of the generated summaries with the overall goal of generating snippets which not only help a user to identify document relevance, but are also designed to increase the user’s understanding and knowledge of a topic gained while inspecting the snippets. We perform a task-based user study to record the user’s interactions, search be- haviour and feedback to evaluate the effectiveness of our snippets using qualitative and quantitative measures. In our user study, we found that richer snippets generated in this work improved the user experience and topical knowledge, and helped users to learn about the topic effectively

    Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

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    The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

    On the Principles of Evaluation for Natural Language Generation

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    Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language. Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation. In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues. We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

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    On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
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