MIR azterketetan erantzun zuzena iragartzeko ezagutza-baseen eta hizkuntza-ereduen erabilpena

Médico Interno Residente (MIR) oso garrantzitsuak diren azterketak dira, Espainian mediku graduatuek egiten dituzte beraiek nahi duten espezialitatean formatzeko. Azterketa horien ebazpenak publikoak ez dira eta gainera, ez dago ia literaturarik MIR azterketetako galderak ebazteko eraikitako sistema desberdinen inguruan. Hori dela eta lan honen helburua MIR azterketak ebazteko sistema bat eraikitzea da. Horretarako, hasteko, medikuntza ezagutza-base handi, aberats eta eleaniztun bat sortu da ezagutza-base desberdinen informazioa bateratuz. Ondoren, aukera egokia iragartzeko asmoz, metodo desberdinak erabiltzen dituzten bi sistema eraiki dira. Batek, sortutako ezagutza-basean dagoen informazioaz baliatuz erantzun egokia iragarri eta arrazoituko du. Besteak, aldiz, lan bat oinarritzat hartuz, ikasketa automatikoko metodoak, hizkuntza-ereduak batez ere, erabiliko ditu erantzun zuzena iragartzeko. Egindako esperimentuek ataza honen zailtasuna agerian jarri dute. Izan ere, bi hurbilpenekin egindako esperimentuetan behin baino ez da %50eko zehaztasuna gainditu. Hala ere, oinarritzat hartu den ereduaren emaitzak hobetzea lortu da, laneko 0,37ko zehaztasuna eta bere eredua gure datuetan ebaluatuz lortutako 0,44ko zehaztasuna, 0,47ko zehaztasunarekin hobetu da. Bestetik, lanaren mugak gainditu dira. Eraikitako eredu berri baten bidez 5 erantzun dituzten galderak ebaztea lortu da eta ezagutza-baseekin horretaz aparte, beste hizkuntza batzuetan idatzita dauden azterketak ebaztea lortu da. Gainera, ezagutza-base oso baliagarri eta aberatsa sortu da aplikazio desberdinak izan ditzakeenak etorkizunean

New frontiers in supervised word sense disambiguation: building multilingual resources and neural models on a large scale

Word Sense Disambiguation is a long-standing task in Natural Language Processing (NLP), lying at the core of human language understanding. While it has already been studied from many different angles over the years, ranging from knowledge based systems to semi-supervised and fully supervised models, the field seems to be slowing down in respect to other NLP tasks, e.g., part-of-speech tagging and dependencies parsing. Despite the organization of several international competitions aimed at evaluating Word Sense Disambiguation systems, the evaluation of automatic systems has been problematic mainly due to the lack of a reliable evaluation framework aiming at performing a direct quantitative confrontation. To this end we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets. Even though supervised systems tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long ShortTerm Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against models trained with engineered features. However, supervised systems need annotated training corpora and the few available to date are of limited size: this is mainly due to the expensive and timeconsuming process of annotating a wide variety of word senses at a reasonably high scale, i.e., the so-called knowledge acquisition bottleneck. To address this issue, we also present different strategies to acquire automatically high quality sense annotated data in multiple languages, without any manual effort. We assess the quality of the sense annotations both intrinsically and extrinsically achieving competitive results on multiple tasks

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge

Mineração e uso de padrões linguísticos para desambiguação de palavras e análise do discurso

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2020.A extração de informação contida em textos na web tem o potencial de alavancar uma série de aplicações, mas muitas delas requerem a captura automática da semântica exata de elementos textuais relevantes. O Twitter, por exemplo, gera diariamente centenas de milhões de pequenos textos (tweets), muitos dos quais com rica informação sobre usuários, fatos, produtos, serviços, desejos, opiniões, etc. A anotação semântica de palavras relevantes em tweets é um grande desafio, pois eles impõem dificuldades adicionais (e.g., pouca informação de contexto, agramaticalidade) para métodos automáticos realizarem uma desambiguação de qualidade, o que leva a resultados com baixa precisão e cobertura. Inclusive, porque a língua é um sistema simbólico polissêmico, que não tem uma semântica pronta, o que se manifesta acentuadamente em linguagem coloquial e particularmente em mídias sociais. As soluções atuais de anotação geralmente não conseguem encontrar o sentido correto de palavras em construções envolvendo a semântica implícita que, às vezes, é colocada intencionalmente, por exemplo, para fazer humor, ironia, jogo de palavras ou trocadilhos. Este trabalho propõe o desenvolvimento de uma abordagem para minerar padrões léxico-semânticos, com a finalidade de captar a semântica em texto para utilizar em tarefas que processam a linguagem. Estes padrões foram denominados de padrões MSC+, pois são definidos por sequências de Componentes Morfo-semânticos (MSC). Um algoritmo não-supervisionado foi desenvolvido para minerar tais padrões, que suportam a identificação de um novo tipo de característica semântica em documentos, assim como métodos para desambiguar o sentido de palavras. Os resultados de experimentos com a tarefa de Word Sense Disambiguation (WSD), em texto de mídia social, mostraram que instâncias de alguns padrões MSC+ aparecem em vários tweets, mas às vezes usando palavras diferentes para transmitir o sentido. Os testes realizados nos resultados do experimento em WSD demonstraram que a exploração dos padrões MSC+ permite mecanismos eficazes na desambiguação do sentido de palavras, levando a melhorias no estado da arte, segundo medidas de precisão, cobertura e medida-F. Os padrões MSC+ também foram explorados em experimentos com Análise do Discurso (AD) do conteúdo de diferentes obras do escritor Machado de Assis. Os experimentos revelaram a incidência de padrões morfo-semânticos que evidenciam características de obras literárias e que podem auxiliar na classificação de discurso das obras analisadas, tais como a preponderância de verbos específicos nos contos, de substantivos femininos nos romances e adjetivos nos poemas.Abstract: Information extraction from social media texts has the potential to boost a number of applications, but many of them require the automatic capture of accurate semantics of relevant textual elements. Twitter, for example, generates hundreds of millions of short texts (tweets) daily, many of which containing rich information about users, facts, products, services, desires, opinions, etc. The semantic annotation of relevant words in tweets is a challenge because social media impose additional difficulties (e.g., little context information, poor grammatical rules conformity) for automatic methods to carry out quality disambiguation. It leads to results with low accuracy and coverage. In addition, a language is a polysemic symbolic system without ready semantics for some constructs. Sometimes words have implicit semantics (e.g., to make humor, irony, wordplay). It is common in colloquial language, and particularly in social media. In this work, we propose the development of an approach to mine lexical-semantic patterns and capture the semantics of texts for use in language processing tasks. We learn these patterns, that we call MSC+ patterns, from text data defined by Morpho-semantic Components (MSC). An unsupervised algorithm was developed to mine such patterns, which support the identification of a new kind of semantic feature in documents, as well as methods for disambiguating the meaning of words. Experimental results on Word Sense Disambiguation (WSD) task, from tweets, show that instances of some MSC+ patterns arise in many tweets, but sometimes using different words to convey the sense of the respective MSC in some tweets where pattern instances appear. The exploitation of MSC+ patterns when they induce semantics on target words enables effective word sense disambiguation mechanisms leading to improvements in the state of the art (e.g., metrics such as accuracy, coverage, and F-measure). We also explored the MSC+ patterns on the Discourse Analysis (DA) with literary content. Experimental results on selected works of a Brazilian writer submitted to our algorithm reveal the incidence of distinct morpho-semantic patterns in different types of works, such as the preponderance of specific verbs in tales, feminine nouns in romances, and adjectives in poems

Learning of a multilingual bitaxonomy of Wikipedia and its application to semantic predicates

The ability to extract hypernymy information on a large scale is becoming increasingly important in natural language processing, an area of the artificial intelligence which deals with the processing and understanding of natural language. While initial studies extracted this type of information from textual corpora by means of lexico-syntactic patterns, over time researchers moved to alternative, more structured sources of knowledge, such as Wikipedia. After the first attempts to extract is-a information fromWikipedia categories, a full line of research gave birth to numerous knowledge bases containing information which, however, is either incomplete or irremediably bound to English. To this end we put forward MultiWiBi, the first approach to the construction of a multilingual bitaxonomy which exploits the inner connection between Wikipedia pages and Wikipedia categories to induce a wide-coverage and fine-grained integrated taxonomy. A series of experiments show state-of-the-art results against all the available taxonomic resources available in the literature, also with respect to two novel measures of comparison. Another dimension where existing resources usually fall short is their degree of multilingualism. While knowledge is typically language agnostic, currently resources are able to extract relevant information only in languages providing highquality tools. In contrast, MultiWiBi does not leave any language behind: we show how to taxonomize Wikipedia in an arbitrary language and in a way that is fully independent of additional resources. At the core of our approach lies, in fact, the idea that the English version of Wikipedia can be linguistically exploited as a pivot to project the taxonomic information extracted from English to any other Wikipedia language in order to have a bitaxonomy in a second, arbitrary language; as a result, not only concepts which have an English equivalent are covered, but also those concepts which are not lexicalized in the source language. We also present the impact of having the taxonomized encyclopedic knowledge offered by MultiWiBi embedded into a semantic model of predicates (SPred) which crucially leverages Wikipedia to generalize collections of related noun phrases to infer a probability distribution over expected semantic classes. We applied SPred to a word sense disambiguation task and show that, when MultiWiBi is plugged in to replace an internal component, SPred’s generalization power increases as well as its precision and recall. Finally, we also published MultiWiBi as linked data, a paradigm which fosters interoperability and interconnection among resources and tools through the publication of data on the Web, and developed a public interface which lets the users navigate through MultiWiBi’s taxonomic structure in a graphical, captivating manner

Engineering Background Knowledge for Social Robots

Social robots are embodied agents that continuously perform knowledge-intensive tasks involving several kinds of information coming from different heterogeneous sources. Providing a framework for engineering robots' knowledge raises several problems like identifying sources of information and modeling solutions suitable for robots' activities, integrating knowledge coming from different sources, evolving this knowledge with information learned during robots' activities, grounding perceptions on robots' knowledge, assessing robots' knowledge with respect humans' one and so on. In this thesis we investigated feasibility and benefits of engineering background knowledge of Social Robots with a framework based on Semantic Web technologies and Linked Data. This research has been supported and guided by a case study that provided a proof of concept through a prototype tested in a real socially assistive context

An Urdu semantic tagger - lexicons, corpora, methods and tools

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports $F_1$ of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F$_1$ of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five 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