A weakly-supervised learning approach to domain-specific semantic tagging

poste

An analysis of gene/protein associations at PubMed scale

<p>Abstract</p> <p>Background</p> <p>Event extraction following the GENIA Event corpus and BioNLP shared task models has been a considerable focus of recent work in biomedical information extraction. This work includes efforts applying event extraction methods to the entire PubMed literature database, far beyond the narrow subdomains of biomedicine for which annotated resources for extraction method development are available.</p> <p>Results</p> <p>In the present study, our aim is to estimate the coverage of all statements of gene/protein associations in PubMed that existing resources for event extraction can provide. We base our analysis on a recently released corpus automatically annotated for gene/protein entities and syntactic analyses covering the entire PubMed, and use named entity co-occurrence, shortest dependency paths and an unlexicalized classifier to identify likely statements of gene/protein associations. A set of high-frequency/high-likelihood association statements are then manually analyzed with reference to the GENIA ontology.</p> <p>Conclusions</p> <p>We present a first estimate of the overall coverage of gene/protein associations provided by existing resources for event extraction. Our results suggest that for event-type associations this coverage may be over 90%. We also identify several biologically significant associations of genes and proteins that are not addressed by these resources, suggesting directions for further extension of extraction coverage.</p

Reducing semantic drift with bagging and distributional similarity

Iterative bootstrapping algorithms are typically compared using a single set of handpicked seeds. However, we demonstrate that performance varies greatly depending on these seeds, and favourable seeds for one algorithm can perform very poorly with others, making comparisons unreliable. We exploit this wide variation with bagging, sampling from automatically extracted seeds to reduce semantic drift. However, semantic drift still occurs in later iterations. We propose an integrated distributional similarity filter to identify and censor potential semantic drifts, ensuring over 10 % higher precision when extracting large semantic lexicons.

Doctor of Philosophy in Computer Science

dissertationOver the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people's affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag in- dicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources

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

Slot Filling

Slot filling (SF) is the task of automatically extracting facts about particular entities from unstructured text, and populating a knowledge base (KB) with these facts. These structured KBs enable applications such as structured web queries and question answering. SF is typically framed as a query-oriented setting of the related task of relation extraction. Throughout this thesis, we reflect on how SF is a task with many distinct problems. We demonstrate that recall is a major limiter on SF system performance. We contribute an analysis of typical SF recall loss, and find a substantial amount of loss occurs early in the SF pipeline. We confirm that accurate NER and coreference resolution are required for high-recall SF. We measure upper bounds using a naïve graph-based semi-supervised bootstrapping technique, and find that only 39% of results are reachable using a typical feature space. We expect that this graph-based technique will be directly useful for extraction, and this leads us to frame SF as a label propagation task. We focus on a detailed graph representation of the task which reflects the behaviour and assumptions we want to model based on our analysis, including modifying the label propagation process to model multiple types of label interaction. Analysing the graph, we find that a large number of errors occur in very close proximity to training data, and identify that this is of major concern for propagation. While there are some conflicts caused by a lack of sufficient disambiguating context—we explore adding additional contextual features to address this—many of these conflicts are caused by subtle annotation problems. We find that lack of a standard for how explicit expressions of relations must be in text makes consistent annotation difficult. Using a strict definition of explicitness results in 20% of correct annotations being removed from a standard dataset. We contribute several annotation-driven analyses of this problem, exploring the definition of slots and the effect of the lack of a concrete definition of explicitness: annotation schema do not detail how explicit expressions of relations need to be, and there is large scope for disagreement between annotators. Additionally, applications may require relatively strict or relaxed evidence for extractions, but this is not considered in annotation tasks. We demonstrate that annotators frequently disagree on instances, dependent on differences in annotator world knowledge and thresholds on making probabilistic inference. SF is fundamental to enabling many knowledge-based applications, and this work motivates modelling and evaluating SF to better target these tasks

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation