Semantic relations between sentences: from lexical to linguistically inspired semantic features and beyond

This thesis is concerned with the identification of semantic equivalence between pairs of natural language sentences, by studying and computing models to address Natural Language Processing tasks where some form of semantic equivalence is assessed. In such tasks, given two sentences, our models output either a class label, corresponding to the semantic relation between the sentences, based on a predefined set of semantic relations, or a continuous score, corresponding to their similarity on a predefined scale. The former setup corresponds to the tasks of Paraphrase Identification and Natural Language Inference, while the latter corresponds to the task of Semantic Textual Similarity. We present several models for English and Portuguese, where various types of features are considered, for instance based on distances between alternative representations of each sentence, following lexical and semantic frameworks, or embeddings from pre-trained Bidirectional Encoder Representations from Transformers models. For English, a new set of semantic features is proposed, from the formal semantic representation of Discourse Representation Structure. In Portuguese, suitable corpora are scarce and formal semantic representations are unavailable, hence an evaluation of currently available features and corpora is conducted, following the modelling setup employed for English. Competitive results are achieved on all tasks, for both English and Portuguese, particularly when considering that our models are based on generally available tools and technologies, and that all features and models are suitable for computation in most modern computers, except for those based on embeddings. In particular, for English, our semantic features from DRS are able to improve the performance of other models, when integrated in the feature set of such models, and state of the art results are achieved for Portuguese, with models based on fine tuning embeddings to a specific task; Sumário: Relações semânticas entre frases: de aspectos lexicais a aspectos semânticos inspirados em linguística e além destes Esta tese é dedicada à identificação de equivalência semântica entre frases em língua natural, através do estudo e computação de modelos destinados a tarefas de Processamento de Linguagem Natural relacionadas com alguma forma de equivalência semântica. Em tais tarefas, a partir de duas frases, os nossos modelos produzem uma etiqueta de classificação, que corresponde à relação semântica entre as frases, baseada num conjunto predefinido de possíveis relações semânticas, ou um valor contínuo, que corresponde à similaridade das frases numa escala predefinida. A primeira configuração mencionada corresponde às tarefas de Identificação de Paráfrases e de Inferência em Língua Natural, enquanto que a última configuração mencionada corresponde à tarefa de Similaridade Semântica em Texto. Apresentamos diversos modelos para Inglês e Português, onde vários tipos de aspectos são considerados, por exemplo baseados em distâncias entre representações alternativas para cada frase, seguindo formalismos semânticos e lexicais, ou vectores contextuais de modelos previamente treinados com Representações Codificadas Bidirecionalmente a partir de Transformadores. Para Inglês, propomos um novo conjunto de aspectos semânticos, a partir da representação formal de semântica em Estruturas de Representação de Discurso. Para Português, os conjuntos de dados apropriados são escassos e não estão disponíveis representações formais de semântica, então implementámos uma avaliação de aspectos actualmente disponíveis, seguindo a configuração de modelos aplicada para Inglês. Obtivemos resultados competitivos em todas as tarefas, em Inglês e Português, particularmente considerando que os nossos modelos são baseados em ferramentas e tecnologias disponíveis, e que todos os nossos aspectos e modelos são apropriados para computação na maioria dos computadores modernos, excepto os modelos baseados em vectores contextuais. Em particular, para Inglês, os nossos aspectos semânticos a partir de Estruturas de Representação de Discurso melhoram o desempenho de outros modelos, quando integrados no conjunto de aspectos de tais modelos, e obtivemos resultados estado da arte para Português, com modelos baseados em afinação de vectores contextuais para certa tarefa

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

...des conférences enfin disons des causeries... Détection automatique de segments en relation de paraphrase dans les reformulations de corpus oraux.

International audienceNotre travail porte sur la détection automatique des segments en relation de reformulation paraphrastique dans les corpus oraux. L'approche proposée est une approche syntagmatique qui tient compte des marqueurs de reformu-lation paraphrastique et des spécificités de l'oral. Les données de référence sont consensuelles. Une méthode automatique fondée sur l'apprentissage avec les CRF est proposée afin de détecter les segments paraphrasés. Différents descripteurs sont exploités dans une fenêtre de taille variable. Les tests effectués montrent que les segments en relation de paraphrase sont assez difficiles à détecter, surtout avec leurs frontières correctes. Les meilleures moyennes atteignent 0,65 de F-mesure, 0,75 de précision et 0,63 de rappel. Nous avons plusieurs perspectives à ce travail pour améliorer la détection des segments en relation de paraphrase et pour étudier les données depuis d'autres points de vue. Abstract. Our work addresses automatic detection of segments with paraphrastic rephrasing relation in spoken corpus. The proposed approach is syntagmatic. It is based on paraphrastic rephrasing markers and the specificities of the spoken language. The reference data used are consensual. Automatic method based on machine learning using CRFs is proposed in order to detect the segments that are paraphrased. Different descriptors are exploited within a window with various sizes. The tests performed indicate that the segments that are in paraphrastic relation are quite difficult to detect. Our best average reaches up to 0.65 F-measure, 0.75 precision, and 0.63 recall. We have several perspectives to this work for improving the detection of segments that are in paraphrastic relation and for studying the data from other points of view

Modelling semantic relations with distributitional semantics and deep learning : question answering, entailment recognition and paraphrase detection

Nesta dissertação apresenta-se uma abordagem à tarefa de modelar relações semânticas entre dois textos com base em modelos de semântica distribucional e em aprendizagem profunda. O presente trabalho tira partido de várias disciplinas da ciência cognitiva, com especial relevo para a computação, a linguística e a inteligência artificial, e com fortes influência da neurociência e da psicologia cognitiva. Os modelos de semântica distribucional (também conhecidos como ”word embeddings”) são usados para representar o significado das palavras. As representações semânticas das palavras podem ainda ser combinadas para obter o significado de um excerto de um texto recorrendo ao uso da aprendizagem profunda, isto é, com o apoio das redes neurais de convolução. Esta abordagen é utilizada para replicar a experiência realizada por Bogdanova et al. (2015) na tarefa de deteção de perguntas que podem ser respondidas as mesmas respostas tal como estas foram respondidas em fóruns on-line. Os resultados do desempenho obtidos pelas experiências apresentadas nesta dissertação são equivalentes ou melhores que os resultados obtidos no trabalho de referência mencionado acima. Apresentao também um estudo sobre o impacto do pré-processamento apropriado do texto, tendo em conta os resultados que podem ser obtidos pelas abordagens adotadas no trabalho de referência supramencionado. Este estudo é levado a cabo removendo-se certas pistas que podem levar o sistema, indevidamente, a detetar perguntas equivalentes. Essa remoção das pistas leva a uma diminuição significativa no desempenho do sistema desenvolvido no trabalho de referência. Nesta dissertação é ainda apresentado um estudo sobre o impacto que os word embeddings treinados previamente têm na tarefa de detetar perguntas semanticamente equivalentes. Substituindo-se, aleatoriamente, word embeddings previamente treinados por outros melhora-se o desempenho do sistema. Além disso, o modelo foi utilizado na tarefa de reconhecimento de implicações para Português, onde mostrou uma taxa de acerto similar à da baseline. Este trabalho também reporta os resultados da aplicação da abordagem adotada numa competição para a deteção de paráfrases em Russo. A configuração final apresenta duas melhorias: usa character embeddings em vez de word embeddings e usa vários filtros de convolução. Esta configuração foi testado na execução padrão da Tarefa 2 da competição relevante, e mostrou resultados competitivos.This dissertation presents an approach to the task of modelling semantic relations between two texts, which is based on distributional semantic models and deep learning. The present work takes advantage of various disciplines of cognitive science, mainly computation, linguistics and artificial intelligence, with strong influences from neuroscience and cognitive psychology. Distributional semantic models (also known as word embeddings) are used to represent the meaning of words. Word semantic representations can be further combined towards obtaining the meaning of a larger chunk of a text using a deep learning approach, namely with the support of convolutional neural networks. These approaches are used to replicate the experiment carried out, by Bogdanova et al. (2015), for the task of detecting questions that can be answered by exactly the same answer in online user forums. Performance results obtained by my experiments are comparable or better than the ones reported in that referenced work. I present also a study on the impact of appropriate text preprocessing with respect to the results that can be obtained by the approaches adopted in that referenced work. Removing certain clues that can unduly help the system to detect equivalent questions leads to a significant decrease in system’s performance supported by that referenced work. I also present a study of the impact that pre-trained word embeddings have in the task of detecting the semantically equivalent questions. Replacing pre-trained word embeddings by randomly initialised ones improves the performance of the system. Additionally, the model was applied to the task of entailment recognition for Portuguese and showed an accuracy on a level with the baseline. This dissertation also reports on the results of an experimental study on the application of the adopted approach to the shared task of sentence paraphrase detection in Russian. The final set up contained two improvements: it uses several convolutional filters and it uses character embeddings instead of word embeddings. It was tested in Task 2 standard run of the relevant shared task and it showed competitive results

Measuring Semantic Similarity: Representations and Methods

This dissertation investigates and proposes ways to quantify and measure semantic similarity between texts. The general approach is to rely on linguistic information at various levels, including lexical, lexico-semantic, and syntactic. The approach starts by mapping texts onto structured representations that include lexical, lexico-semantic, and syntactic information. The representation is then used as input to methods designed to measure the semantic similarity between texts based on the available linguistic information.While world knowledge is needed to properly assess semantic similarity of texts, in our approach world knowledge is not used, which is a weakness of it.We limit ourselves to answering the question of how successfully one can measure the semantic similarity of texts using just linguistic information.The lexical information in the original texts is retained by using the words in the corresponding representations of the texts. Syntactic information is encoded using dependency relations trees, which represent explicitly the syntactic relations between words. Word-level semantic information is relatively encoded through the use of semantic similarity measures like WordNet Similarity or explicitly encoded using vectorial representations such as Latent Semantic Analysis (LSA). Several methods are being studied to compare the representations, ranging from simple lexical overlap, to more complex methods such as comparing semantic representations in vector spaces as well as syntactic structures. Furthermore, a few powerful kernel models are proposed to use in combination with Support Vector Machine (SVM) classifiers for the case in which the semantic similarity problem is modeled as a classification task

A resource-light method for cross-lingual semantic textual similarity

[EN] Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. (C) 2017 Published by Elsevier B.V.Part of the work presented in this article was performed during second author's research visit to the University of Mannheim, supported by Contact Fellowship awarded by the DAAD scholarship program "STIBET Doktoranden". The research of the last author has been carried out in the framework of the SomEMBED project (TIN2015-71147-C2-1-P). Furthermore, this work was partially funded by the Junior-professor funding programme of the Ministry of Science, Research and the Arts of the state of Baden-Wurttemberg (project "Deep semantic models for high-end NLP application").Glavas, G.; Franco-Salvador, M.; Ponzetto, SP.; Rosso, P. (2018). A resource-light method for cross-lingual semantic textual similarity. Knowledge-Based Systems. 143:1-9. https://doi.org/10.1016/j.knosys.2017.11.041S1914

Detecção de Paráfrases na Lı́ngua Portuguesa usando Sentence Embeddings

A detecção (ou identificação) de paráfrases é a tarefa de determinar se duas ou mais sentenças de comprimento arbitrário possuem o mesmo significado. Os métodos para resolver esta tarefa com potenciais aplicações em sistemas de Processamento de Linguagem Natural. Este trabalho investiga a combinação de diferentes métodos de representação de sentenças em modelos de linguagem por espaços vetoriais e classificadores lineares para o problema de detecção de paráfrases para a língua portuguesa. Os resultados obtidos nesse trabalho estão aquém daqueles obtidos para a tarefa relacionada de detecção de implicação textual na avaliação ASSIN para a língua portuguesa, porém nesse trabalho investigamos a aplicação das representações vetoriais de sentenças para a detecção de paráfrases, outras características usualmente exploradas em sistemas desse tipo podem trivialmente ser incorporadas ao nosso método para melhorar a performance