Temporality and modality in entailment graph induction

The ability to draw inferences is core to semantics and the field of Natural Language Processing. Answering a seemingly simple question like ‘Did Arsenal play Manchester yesterday’ from textual evidence that says ‘Arsenal won against Manchester yesterday’ requires modeling the inference that ‘winning’ entails ‘playing’. One way of modeling this type of lexical semantics is with Entailment Graphs, collections of meaning postulates that can be learned in an unsupervised way from large text corpora. In this work, we explore the role that temporality and linguistic modality can play in inducing Entailment Graphs. We identify inferences that were previously not supported by Entailment Graphs (such as that ‘visiting’ entails an ‘arrival’ before the visit) and inferences that were likely to be learned incorrectly (such as that ‘winning’ entails ‘losing’). Temporality is shown to be useful in alleviating these challenges, in the Entailment Graph representation as well as the learning algorithm. An exploration of linguistic modality in the training data shows, counterintuitively, that there is valuable signal in modalized predications. We develop three datasets for evaluating a system’s capability of modeling these inferences, which were previously underrepresented in entailment rule evaluations. Finally, in support of the work on modality, we release a relation extraction system that is capable of annotating linguistic modality, together with a comprehensive modality lexicon

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding

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

Knowledge base integration in biomedical natural language processing applications

With the progress of natural language processing in the biomedical field, the lack of annotated data due to regulations and expensive labor remains an issue. In this work, we study the potential of knowledge bases for biomedical language processing to compensate for the shortage of annotated data. Accordingly, we experiment with the integration of a rigorous biomedical knowledge base, the Unified Medical Language System, in three different biomedical natural language processing applications: text simplification, conversational agents for medication adherence, and automatic evaluation of medical students' chart notes. In the first task, we take as a use case simplifying medication instructions to enhance medication adherence among patients. Given the lack of an appropriate parallel corpus, the Unified Medical Language System provided simpler synonyms for an unsupervised system we devise, and we show a positive impact on comprehension through a human subjects study. As for the second task, we devise an unsupervised system to automatically evaluate chart notes written by medical students. The purpose of the system is to speed up the feedback process and enhance the educational experience. With the lack of training corpora, utilizing the Unified Medical Language System proved to enhance the accuracy of evaluation after integration into the baseline system. For the final task, the Unified Medical Language System was used to augment the training data of a conversational agent that educates patients on their medications. As part of the educational procedure, the agent needed to assess the comprehension of the patients by evaluating their answers to predefined questions. Starting with a small seed set of paraphrases of acceptable answers, the Unified Medical Language System was used to artificially augment the original small seed set via synonymy. Results did not show an increase in quality of system output after knowledge base integration due to the majority of errors resulting from mishandling of counts and negations. We later demonstrate the importance of a (lacking) entity linking system to perform optimal integration of biomedical knowledge bases, and we offer a first stride towards solving that problem, along with conclusions on proper training setup and processes for automatic collection of an annotated dataset for biomedical word sense disambiguation

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains