Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e.~150 sentences per language

Automatic stance detection on political discourse in Twitter

The majority of opinion mining tasks in natural language processing (NLP) have been focused on sentiment analysis of texts about products and services while there is comparatively less research on automatic detection of political opinion. Almost all previous research work has been done for English, while this thesis is focused on the automatic detection of stance (whether he or she is favorable or not towards important political topic) from Twitter posts in Catalan, Spanish and English. The main objective of this work is to build and compare automatic stance detection systems using supervised both classic machine and deep learning techniques. We also study the influence of text normalization and perform experiments with differentt methods for word representations such as TF-IDF measures for unigrams, word embeddings, tweet embeddings, and contextual character-based embeddings. We obtain state-of-the-art results in the stance detection task on the IberEval 2018 dataset. Our research shows that text normalization and feature selection is important for the systems with unigram features, and does not affect the performance when working with word vector representations. Classic methods such as unigrams and SVM classifier still outperform deep learning techniques, but seem to be prone to overfitting. The classifiers trained using word vector representations and the neural network models encoded with contextual character-based vectors show greater robustness

Exploiting word embeddings for modeling bilexical relations

There has been an exponential surge of text data in the recent years. As a consequence, unsupervised methods that make use of this data have been steadily growing in the field of natural language processing (NLP). Word embeddings are low-dimensional vectors obtained using unsupervised techniques on the large unlabelled corpora, where words from the vocabulary are mapped to vectors of real numbers. Word embeddings aim to capture syntactic and semantic properties of words. In NLP, many tasks involve computing the compatibility between lexical items under some linguistic relation. We call this type of relation a bilexical relation. Our thesis defines statistical models for bilexical relations that centrally make use of word embeddings. Our principle aim is that the word embeddings will favor generalization to words not seen during the training of the model. The thesis is structured in four parts. In the first part of this thesis, we present a bilinear model over word embeddings that leverages a small supervised dataset for a binary linguistic relation. Our learning algorithm exploits low-rank bilinear forms and induces a low-dimensional embedding tailored for a target linguistic relation. This results in compressed task-specific embeddings. In the second part of our thesis, we extend our bilinear model to a ternary setting and propose a framework for resolving prepositional phrase attachment ambiguity using word embeddings. Our models perform competitively with state-of-the-art models. In addition, our method obtains significant improvements on out-of-domain tests by simply using word-embeddings induced from source and target domains. In the third part of this thesis, we further extend the bilinear models for expanding vocabulary in the context of statistical phrase-based machine translation. Our model obtains a probabilistic list of possible translations of target language words, given a word in the source language. We do this by projecting pre-trained embeddings into a common subspace using a log-bilinear model. We empirically notice a significant improvement on an out-of-domain test set. In the final part of our thesis, we propose a non-linear model that maps initial word embeddings to task-tuned word embeddings, in the context of a neural network dependency parser. We demonstrate its use for improved dependency parsing, especially for sentences with unseen words. We also show downstream improvements on a sentiment analysis task.En els darrers anys hi ha hagut un sorgiment notable de dades en format textual. Conseqüentment, en el camp del Processament del Llenguatge Natural (NLP, de l'anglès "Natural Language Processing") s'han desenvolupat mètodes no supervistats que fan ús d'aquestes dades. Els anomenats "word embeddings", o embeddings de paraules, són vectors de dimensionalitat baixa que s'obtenen mitjançant tècniques no supervisades aplicades a corpus textuals de grans volums. Com a resultat, cada paraula del diccionari es correspon amb un vector de nombres reals, el propòsit del qual és capturar propietats sintàctiques i semàntiques de la paraula corresponent. Moltes tasques de NLP involucren calcular la compatibilitat entre elements lèxics en l'àmbit d'una relació lingüística. D'aquest tipus de relació en diem relació bilèxica. Aquesta tesi proposa models estadístics per a relacions bilèxiques que fan ús central d'embeddings de paraules, amb l'objectiu de millorar la generalització del model lingüístic a paraules no vistes durant l'entrenament. La tesi s'estructura en quatre parts. A la primera part presentem un model bilineal sobre embeddings de paraules que explota un conjunt petit de dades anotades sobre una relaxió bilèxica. L'algorisme d'aprenentatge treballa amb formes bilineals de poc rang, i indueix embeddings de poca dimensionalitat que estan especialitzats per la relació bilèxica per la qual s'han entrenat. Com a resultat, obtenim embeddings de paraules que corresponen a compressions d'embeddings per a una relació determinada. A la segona part de la tesi proposem una extensió del model bilineal a trilineal, i amb això proposem un nou model per a resoldre ambigüitats de sintagmes preposicionals que usa només embeddings de paraules. En una sèrie d'avaluacións, els nostres models funcionen de manera similar a l'estat de l'art. A més, el nostre mètode obté millores significatives en avaluacions en textos de dominis diferents al d'entrenament, simplement usant embeddings induïts amb textos dels dominis d'entrenament i d'avaluació. A la tercera part d'aquesta tesi proposem una altra extensió dels models bilineals per ampliar la cobertura lèxica en el context de models estadístics de traducció automàtica. El nostre model probabilístic obté, donada una paraula en la llengua d'origen, una llista de possibles traduccions en la llengua de destí. Fem això mitjançant una projecció d'embeddings pre-entrenats a un sub-espai comú, usant un model log-bilineal. Empíricament, observem una millora significativa en avaluacions en dominis diferents al d'entrenament. Finalment, a la quarta part de la tesi proposem un model no lineal que indueix una correspondència entre embeddings inicials i embeddings especialitzats, en el context de tasques d'anàlisi sintàctica de dependències amb models neuronals. Mostrem que aquest mètode millora l'analisi de dependències, especialment en oracions amb paraules no vistes durant l'entrenament. També mostrem millores en un tasca d'anàlisi de sentiment

Deep neural networks for identification of sentential relations

Natural language processing (NLP) is one of the most important technologies in the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate mostly in language: web search, advertisement, emails, customer service, language translation, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained exciting performance across a broad array of NLP tasks. These models can often be trained in an end-to-end paradigm without traditional, task-specific feature engineering. This dissertation focuses on a specific NLP task --- sentential relation identification. Successfully identifying the relations of two sentences can contribute greatly to some downstream NLP problems. For example, in open-domain question answering, if the system can recognize that a new question is a paraphrase of a previously observed question, the known answers can be returned directly, avoiding redundant reasoning. For another, it is also helpful to discover some latent knowledge, such as inferring ``the weather is good today'' from another description ``it is sunny today''. This dissertation presents some deep neural networks (DNNs) which are developed to handle this sentential relation identification problem. More specifically, this problem is addressed by this dissertation in the following three aspects. (i) Sentential relation representation is built on the matching between phrases of arbitrary lengths. Stacked Convolutional Neural Networks (CNNs) are employed to model the sentences, so that each filter can cover a local phrase, and filters in lower level span shorter phrases and filters in higher level span longer phrases. CNNs in stack enable to model sentence phrases in different granularity and different abstraction. (ii) Phrase matches contribute differently to the tasks. This motivates us to propose an attention mechanism in CNNs for these tasks, differing from the popular research of attention mechanisms in Recurrent Neural Networks (RNNs). Attention mechanisms are implemented in both convolution layer as well as pooling layer in deep CNNs, in order to figure out automatically which phrase of one sentence matches a specific phrase of the other sentence. These matches are supposed to be indicative to the final decision. Another contribution in terms of attention mechanism is inspired by the observation that some sentential relation identification task, like answer selection for multi-choice question answering, is mainly determined by phrase alignments of stronger degree; in contrast, some tasks such as textual entailment benefit more from the phrase alignments of weaker degree. This motivates us to propose a dynamic ``attentive pooling'' to select phrase alignments of different intensities for different task categories. (iii) In certain scenarios, sentential relation can only be successfully identified within specific background knowledge, such as the multi-choice question answering based on passage comprehension. In this case, the relation between two sentences (question and answer candidate) depends on not only the semantics in the two sentences, but also the information encoded in the given passage. Overall, the work in this dissertation models sentential relations in hierarchical DNNs, different attentions and different background knowledge. All systems got state-of-the-art performances in representative tasks.Die Verarbeitung natürlicher Sprachen (engl.: natural language processing - NLP) ist eine der wichtigsten Technologien des Informationszeitalters. Weiterhin ist das Verstehen komplexer sprachlicher Ausdrücke ein essentieller Teil künstlicher Intelligenz. Anwendungen von NLP sind überall zu finden, da Menschen haupt\-säch\-lich über Sprache kommunizieren: Internetsuchen, Werbung, E-Mails, Kundenservice, Übersetzungen, etc. Es gibt eine große Anzahl Tasks und Modelle des maschinellen Lernens für NLP-Anwendungen. In den letzten Jahren haben Deep-Learning-Ansätze vielversprechende Ergebnisse für eine große Anzahl verschiedener NLP-Tasks erzielt. Diese Modelle können oft end-to-end trainiert werden, kommen also ohne auf den Task zugeschnittene Feature aus. Diese Dissertation hat einen speziellen NLP-Task als Fokus: Sententielle Relationsidentifizierung. Die Beziehung zwischen zwei Sätzen erfolgreich zu erkennen, kann die Performanz für nachfolgende NLP-Probleme stark verbessern. Für open-domain question answering, zum Beispiel, kann ein System, das erkennt, dass eine neue Frage eine Paraphrase einer bereits gesehenen Frage ist, die be\-kann\-te Antwort direkt zurückgeben und damit mehrfaches Schlussfolgern vermeiden. Zudem ist es auch hilfreich, zu Grunde liegendes Wissen zu entdecken, so wie das Schließen der Tatsache "das Wetter ist gut" aus der Beschreibung "es ist heute sonnig". Diese Dissertation stellt einige tiefe neuronale Netze (eng.: deep neural networks - DNNs) vor, die speziell für das Problem der sententiellen Re\-la\-tions\-i\-den\-ti\-fi\-zie\-rung entwickelt wurden. Im Speziellen wird dieses Problem in dieser Dissertation unter den folgenden drei Aspekten behandelt: (i) Sententielle Relationsrepr\"{a}sentationen basieren auf einem Matching zwischen Phrasen beliebiger Länge. Tiefe convolutional neural networks (CNNs) werden verwendet, um diese Sätze zu modellieren, sodass jeder Filter eine lokale Phrase abdecken kann, wobei Filter in niedrigeren Schichten kürzere und Filter in höheren Schichten längere Phrasen umfassen. Tiefe CNNs machen es möglich, Sätze in unterschiedlichen Granularitäten und Abstraktionsleveln zu modellieren. (ii) Matches zwischen Phrasen tragen unterschiedlich zu unterschiedlichen Tasks bei. Das motiviert uns, einen Attention-Mechanismus für CNNs für diese Tasks einzuführen, der sich von dem bekannten Attention-Mechanismus für recurrent neural networks (RNNs) unterscheidet. Wir implementieren Attention-Mechanismen sowohl im convolution layer als auch im pooling layer tiefer CNNs, um herauszufinden, welche Phrasen eines Satzes bestimmten Phrasen eines anderen Satzes entsprechen. Wir erwarten, dass solche Matches die finale Entscheidung stark beeinflussen. Ein anderer Beitrag zu Attention-Mechanismen wurde von der Beobachtung inspiriert, dass einige sententielle Relationsidentifizierungstasks, zum Beispiel die Auswahl einer Antwort für multi-choice question answering hauptsächlich von Phrasen\-a\-lignie\-rungen stärkeren Grades bestimmt werden. Im Gegensatz dazu profitieren andere Tasks wie textuelles Schließen mehr von Phrasenalignierungen schwächeren Grades. Das motiviert uns, ein dynamisches "attentive pooling" zu entwickeln, um Phrasenalignierungen verschiedener Stärken für verschiedene Taskkategorien auszuwählen. (iii) In bestimmten Szenarien können sententielle Relationen nur mit entsprechendem Hintergrundwissen erfolgreich identifiziert werden, so wie multi-choice question answering auf der Grundlage des Verständnisses eines Absatzes. In diesem Fall hängt die Relation zwischen zwei Sätzen (der Frage und der möglichen Antwort) nicht nur von der Semantik der beiden Sätze, sondern auch von der in dem gegebenen Absatz enthaltenen Information ab. Insgesamt modellieren die in dieser Dissertation enthaltenen Arbeiten sententielle Relationen in hierarchischen DNNs, mit verschiedenen Attention-Me\-cha\-nis\-men und wenn unterschiedliches Hintergrundwissen zur Verf\ {u}gung steht. Alle Systeme erzielen state-of-the-art Ergebnisse für die entsprechenden Tasks