Event Extraction: A Survey

Extracting the reported events from text is one of the key research themes in natural language processing. This process includes several tasks such as event detection, argument extraction, role labeling. As one of the most important topics in natural language processing and natural language understanding, the applications of event extraction spans across a wide range of domains such as newswire, biomedical domain, history and humanity, and cyber security. This report presents a comprehensive survey for event detection from textual documents. In this report, we provide the task definition, the evaluation method, as well as the benchmark datasets and a taxonomy of methodologies for event extraction. We also present our vision of future research direction in event detection.Comment: 20 page

Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction

In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE). The paper identifies key challenges in this problem, including example selection, context length limitation, abundance of event types, and the limitation of Chain-of-Thought (CoT) prompting in non-reasoning tasks. To address these challenges, we introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations via ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a methodical method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their adaptability. Extensive experiments show that our method outperforms the existing prompting methods and few-shot supervised learning methods, exhibiting F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset. Furthermore, when applied to sentiment analysis and natural language inference tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%, indicating its effectiveness across different tasks

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting

Machine Learning and Natural Language Processing in Stock Prediction

In this thesis, we first study the two ill-posed natural language processing tasks related to stock prediction, i.e. stock movement prediction and financial document-level event extraction. While implementing stock prediction and event extraction, we encountered difficulties that could be resolved by utilizing out-of-distribution detection. Consequently, we presented a new approach for out-of-distribution detection, which is the third focus of this thesis. First, we systematically build a platform to study the NLP-aided stock auto-trading algorithms. Our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. We also propose a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labelling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the stock movement prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all strong baselines’ annualized rate of return as well as the maximum drawdown in back-testing. Second, we propose a generative solution for document-level event extraction that takes into account recent developments in generative event extraction, which have been successful at the sentence level but have not yet been explored for document-level extraction. Our proposed solution includes an encoding scheme to capture entity-to-document level information and a decoding scheme that takes into account all relevant contexts. Extensive experimental results demonstrate that our generative-based solution can perform as well as state-of-theart methods that use specialized structures for document event extraction. This allows our method to serve as an easy-to-use and strong baseline for future research in this area. Finally, we propose a new unsupervised OOD detection model that separates, extracts, and learns the semantic role labelling guided fine-grained local feature representation from different sentence arguments and the full sentence using a margin-based contrastive loss. Then we demonstrate the benefit of applying a self-supervised approach to enhance such global-local feature learning by predicting the SRL extracted role. We conduct our experiments and achieve state-of-the-art performance on out-of-distribution benchmarks.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

The Role of Linguistics in Probing Task Design

Over the past decades natural language processing has evolved from a niche research area into a fast-paced and multi-faceted discipline that attracts thousands of contributions from academia and industry and feeds into real-world applications. Despite the recent successes, natural language processing models still struggle to generalize across domains, suffer from biases and lack transparency. Aiming to get a better understanding of how and why modern NLP systems make their predictions for complex end tasks, a line of research in probing attempts to interpret the behavior of NLP models using basic probing tasks. Linguistic corpora are a natural source of such tasks, and linguistic phenomena like part of speech, syntax and role semantics are often used in probing studies. The goal of probing is to find out what information can be easily extracted from a pre-trained NLP model or representation. To ensure that the information is extracted from the NLP model and not learned during the probing study itself, probing models are kept as simple and transparent as possible, exposing and augmenting conceptual inconsistencies between NLP models and linguistic resources. In this thesis we investigate how linguistic conceptualization can affect probing models, setups and results. In Chapter 2 we investigate the gap between the targets of classical type-level word embedding models like word2vec, and the items of lexical resources and similarity benchmarks. We show that the lack of conceptual alignment between word embedding vocabularies and lexical resources penalizes the word embedding models in both benchmark-based and our novel resource-based evaluation scenario. We demonstrate that simple preprocessing techniques like lemmatization and POS tagging can partially mitigate the issue, leading to a better match between word embeddings and lexicons. Linguistics often has more than one way of describing a certain phenomenon. In Chapter 3 we conduct an extensive study of the effects of lingustic formalism on probing modern pre-trained contextualized encoders like BERT. We use role semantics as an excellent example of a data-rich multi-framework phenomenon. We show that the choice of linguistic formalism can affect the results of probing studies, and deliver additional insights on the impact of dataset size, domain, and task architecture on probing. Apart from mere labeling choices, linguistic theories might differ in the very way of conceptualizing the task. Whereas mainstream NLP has treated semantic roles as a categorical phenomenon, an alternative, prominence-based view opens new opportunities for probing. In Chapter 4 we investigate prominence-based probing models for role semantics, incl. semantic proto-roles and our novel regression-based role probe. Our results indicate that pre-trained language models like BERT might encode argument prominence. Finally, we propose an operationalization of thematic role hierarchy - a widely used linguistic tool to describe syntactic behavior of verbs, and show that thematic role hierarchies can be extracted from text corpora and transfer cross-lingually. The results of our work demonstrate the importance of linguistic conceptualization for probing studies, and highlight the dangers and the opportunities associated with using linguistics as a meta-langauge for NLP model interpretation

SCOPE: Building and Testing an Integrated Manual-Automated Event Extraction Tool for Online Text-Based Media Sources

Building on insights from two years of manually extracting events information from online news media, an interactive information extraction environment (IIEE) was developed. SCOPE, the Scientific Collection of Open-source Policy Evidence, is a Python Django-based tool divided across specialized modules for extracting structured events data from unstructured text. These modules are grouped into a flexible framework which enables the user to tailor the tool to meet their needs. Following principles of user-oriented learning for information extraction (IE), SCOPE offers an alternative approach to developing AI-assisted IE systems. In this piece, we detail the ongoing development of the SCOPE tool, present methods and results of tests of the efficacy of SCOPE relative to past methods, and provide a novel framework for future tests of AI-assisted IE tasks. Information gathered from a four-week period of use was analyzed to evaluate the initial utility of the tool and establish baseline accuracy metrics. Using the SCOPE tool, 15 users extracted 529 summaries and 362 structured events from 207 news articles achieving an accuracy of 31.8% holding time constant at 4 minutes per source. To demonstrate how fully or partially-automated AI processes can be integrated into SCOPE, a baseline AI was implemented and achieved 4.8% accuracy at 3.25 seconds per source. These results illustrate the ability of SCOPE to present the relative strengths and weaknesses of manual users and AI, as well as establish precedent and methods for integrating the two