Weakly-supervised Learning Approaches for Event Knowledge Acquisition and Event Detection

Capabilities of detecting events and recognizing temporal, subevent, or causality relations among events can facilitate many applications in natural language understanding. However, supervised learning approaches that previous research mainly uses have two problems. First, due to the limited size of annotated data, supervised systems cannot sufficiently capture diverse contexts to distill universal event knowledge. Second, under certain application circumstances such as event recognition during emergent natural disasters, it is infeasible to spend days or weeks to annotate enough data to train a system. My research aims to use weakly-supervised learning to address these problems and to achieve automatic event knowledge acquisition and event recognition. In this dissertation, I first introduce three weakly-supervised learning approaches that have been shown effective in acquiring event relational knowledge. Firstly, I explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts, and these rich contexts can be used to train a contextual temporal relation classifier to further recognize new temporal relation knowledge. Secondly, inspired by the double temporality characteristic of narrative texts, I propose a weakly supervised approach that identifies 287k narrative paragraphs using narratology principles and then extract rich temporal event knowledge from identified narratives. Lastly, I develop a subevent knowledge acquisition approach by exploiting two observations that 1) subevents are temporally contained by the parent event and 2) the definitions of the parent event can be used to guide the identification of subevents. I collect rich weak supervision to train a contextual BERT classifier and apply the classifier to identify new subevent knowledge. Recognizing texts that describe specific categories of events is also challenging due to language ambiguity and diverse descriptions of events. So I also propose a novel method to rapidly build a fine-grained event recognition system on social media texts for disaster management. My method creates high-quality weak supervision based on clustering-assisted word sense disambiguation and enriches tweet message representations using preceding context tweets and reply tweets in building event recognition classifiers

Powerful Prose: How Textual Features Impact Readers

What makes a reading experience »powerful«? This volume brings together literary scholars, linguists, and empirical researchers who tackle the question by investigating the effects and reader responses generated by selected extracts of literary prose. The twelve contributions theorize this widely-used, but to date insufficiently studied notion, and provide insights into the therefore still mysterious-seeming power of literary fiction. The collection explores a variety of stylistic as well as readerly and psychological features responsible for short- and long-term effects - topics of great interest to those interested or specialized in literary studies and narratology, (cognitive) stylistics, empirical literary studies and reader response theory

Powerful Prose

What makes a reading experience »powerful«? This volume brings together literary scholars, linguists, and empirical researchers to elucidate the effects and reader responses to investigate just that. The thirteen contributions theorize this widely-used, but to date insufficiently studied notion, and provide insights into the therefore still mysterious-seeming power of literary fiction. The collection investigates a variety of stylistic as well as readerly and psychological features responsible for short- and long-term effects - topics of great interest to those interested or specialized in literary studies and narratology, (cognitive) stylistics, empirical literary studies and reader response theory

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Decompositional Semantics for Events, Participants, and Scripts in Text

This thesis presents a sequence of practical and conceptual developments in decompositional meaning representations for events, participants, and scripts in text under the framework of Universal Decompositional Semantics (UDS) (White et al., 2016a). Part I of the thesis focuses on the semantic representation of individual events and their participants. Chapter 3 examines the feasibility of deriving semantic representations of events from dependency syntax; we demonstrate that predicate- argument structure may be extracted from syntax, but other desirable semantic attributes are not directly discernible. Accordingly, we present in Chapters 4 and 5 state of the art models for predicting these semantic attributes from text. Chapter 4 presents a model for predicting semantic proto-role labels (SPRL), attributes of participants in events based on Dowty’s seminal theory of thematic proto-roles (Dowty, 1991). In Chapter 5 we present a model of event factuality prediction (EFP), the task of determining whether an event mentioned in text happened (according to the meaning of the text). Both chapters include extensive experiments on multi-task learning for improving performance on each semantic prediction task. Taken together, Chapters 3, 4, and 5 represent the development of individual components of a UDS parsing pipeline. In Part II of the thesis, we shift to modeling sequences of events, or scripts (Schank and Abelson, 1977). Chapter 7 presents a case study in script induction using a collection of restaurant narratives from an online blog to learn the canonical “Restaurant Script.” In Chapter 8, we introduce a simple discriminative neural model for script induction based on narrative chains (Chambers and Jurafsky, 2008) that outperforms prior methods. Because much existing work on narrative chains employs semantically impoverished representations of events, Chapter 9 draws on the contributions of Part I to learn narrative chains with semantically rich, decompositional event representations. Finally, in Chapter 10, we observe that corpus based approaches to script induction resemble the task of language modeling. We explore the broader question of the relationship between language modeling and acquisition of common-sense knowledge, and introduce an approach that combines language modeling and light human supervision to construct datasets for common-sense inference

A Socio-mathematical and Structure-Based Approach to Model Sentiment Dynamics in Event-Based Text

Natural language texts are often meant to express or impact the emotions of individuals. Recognizing the underlying emotions expressed in or triggered by textual content is essential if one is to arrive at an understanding of the full meaning that textual content conveys. Sentiment analysis (SA) researchers are becoming increasingly interested in investigating natural language processing techniques as well as emotion theory in order to detect, extract, and classify the sentiments that natural language text expresses. Most SA research is focused on the analysis of subjective documents from the writer’s perspective and their classification into categorical labels or sentiment polarity, in which text is associated with a descriptive label or a point on a continuum between two polarities. Researchers often perform sentiment or polarity classification tasks using machine learning (ML) techniques, sentiment lexicons, or hybrid-based approaches. Most ML methods rely on count-based word representations that fail to take word order into account. Despite the successful use of these flat word representations in topic-modelling problems, SA problems require a deeper understanding of sentence structure, since the entire meaning of words can be reversed through negations or word modifiers. On the other hand, approaches based on semantic lexicons are limited by the relatively small number of words they contain, which do not begin to embody the extensive and growing vocabulary on the Internet. The research presented in this thesis represents an effort to tackle the problem of sentiment analysis from a different viewpoint than those underlying current mainstream studies in this research area. A cross-disciplinary approach is proposed that incorporates affect control theory (ACT) into a structured model for determining the sentiment polarity of event-based articles from the perspectives of readers and interactants. A socio-mathematical theory, ACT provides valuable resources for handling interactions between words (event entities) and for predicting situational sentiments triggered by social events. ACT models human emotions arising from social event terms through the use of multidimensional representations that have been verified both empirically and theoretically. To model human emotions regarding textual content, the first step was to develop a fine-grained event extraction algorithm that extracts events and their entities from event-based textual information using semantic and syntactic parsing techniques. The results of the event extraction method were compared against a supervised learning approach on two human-coded corpora (a grammatically correct and a grammatically incorrect structured corpus). For both corpora, the semantic-syntactic event extraction method yielded a higher degree of accuracy than the supervised learning approach. The three-dimensional ACT lexicon was also augmented in a semi-supervised fashion using graph-based label propagation built from semantic and neural network word embeddings. The word embeddings were obtained through the training of commonly used count-based and neural-network-based algorithms on a single corpus, and each method was evaluated with respect to the reconstruction of a sentiment lexicon. The results show that, relative to other word embeddings and state-of-the-art methods, combining both semantic and neural word embeddings yielded the highest correlation scores and lowest error rates. Using the augmented lexicon and ACT mathematical equations, human emotions were modelled according to different levels of granularity (i.e., at the sentence and document levels). The initial stage involved the development of a proposed entity-based SA approach that models reader emotions triggered by event-based sentences. The emotions are modelled in a three-dimensional space based on reader sentiment toward different entities (e.g., subject and object) in the sentence. The new approach was evaluated using a human-annotated news-headline corpus; the results revealed the proposed method to be competitive with benchmark ML techniques. The second phase entailed the creation of a proposed ACT-based model for predicting the temporal progression of the emotions of the interactants and their optimal behaviour over a sequence of interactions. The model was evaluated using three different corpora: fairy tales, news articles, and a handcrafted corpus. The results produced by the proposed model demonstrate that, despite the challenging sentence structure, a reasonable agreement was achieved between the estimated emotions and behaviours and the corresponding ground truth

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi