Event Detection and Extraction from News Articles
| dc.contributor.author | Wang, Wei | en |
| dc.contributor.committeechair | Ramakrishnan, Naren | en |
| dc.contributor.committeemember | North, Christopher L. | en |
| dc.contributor.committeemember | Tandon, Ravi | en |
| dc.contributor.committeemember | Lu, Chang-Tien | en |
| dc.contributor.committeemember | Rangwala, Huzefa | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2018-02-22T09:00:37Z | en |
| dc.date.available | 2018-02-22T09:00:37Z | en |
| dc.date.issued | 2018-02-21 | en |
| dc.description.abstract | Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly. | en |
| dc.description.abstractgeneral | Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. The demand of making use of the massive on-line information during decision making process becomes increasing intensive. Therefore, it is imperative to develop algorithms that automatically extract the formatted information from large volumes of the unstructured text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves detecting the event and extracting key information about the event in the article. (3) Third, the efforts concentrate on extracting the arguments of the event from the text. We found that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of current event extraction systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Our experiments on two real-world event extraction tasks (Cyber-Attack and Civil Unrest) show the effectiveness of our deep learning approaches for detecting and extracting the event information from unstructured text data. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12929 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/82238 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Event Detection | en |
| dc.subject | Event Encoding | en |
| dc.subject | Deep learning (Machine learning) | en |
| dc.subject | Convolutional Neural Network | en |
| dc.subject | Recurrent Neural Network | en |
| dc.subject | Multi Instance Learning | en |
| dc.subject | Multi Task Learning | en |
| dc.title | Event Detection and Extraction from News Articles | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
Files
Original bundle
1 - 1 of 1