Few-Shot and Zero-Shot Learning for Information Extraction
| dc.contributor.author | Gong, Jiaying | en |
| dc.contributor.committeechair | Eldardiry, Hoda Mohamed | en |
| dc.contributor.committeemember | Huang, Jia-Bin | en |
| dc.contributor.committeemember | Lourentzou, Ismini | en |
| dc.contributor.committeemember | Zhou, Dawei | en |
| dc.contributor.committeemember | Huang, Lifu | en |
| dc.contributor.department | Computer Science and#38; Applications | en |
| dc.date.accessioned | 2024-06-01T08:02:15Z | en |
| dc.date.available | 2024-06-01T08:02:15Z | en |
| dc.date.issued | 2024-05-31 | en |
| dc.description.abstract | Information extraction aims to automatically extract structured information from unstructured texts. Supervised information extraction requires large quantities of labeled training data, which is time-consuming and labor-intensive. This dissertation focuses on information extraction, especially relation extraction and attribute-value extraction in e-commerce, with few labeled (few-shot learning) or even no labeled (zero-shot learning) training data. We explore multi-source auxiliary information and novel learning techniques to integrate semantic auxiliary information with the input text to improve few-shot learning and zero-shot learning. For zero-shot and few-shot relation extraction, the first method explores the existing data statistics and leverages auxiliary information including labels, synonyms of labels, keywords, and hypernyms of name entities to enable zero-shot learning for the unlabeled data. We build an automatic hypernym extraction framework to help acquire hypernyms of different entities directly from the web. The second method explores the relations between seen classes and new classes. We propose a prompt-based model with semantic knowledge augmentation to recognize new relation triplets under the zero-shot setting. In this method, we transform the problem of zero-shot learning into supervised learning with the generated augmented data for new relations. We design the prompts for training using the auxiliary information based on an external knowledge graph to integrate semantic knowledge learned from seen relations. The third work utilizes auxiliary information from images to enhance few-shot learning. We propose a multi-modal few-shot relation extraction model that leverages both textual and visual semantic information to learn a multi-modal representation jointly. To supplement the missing contexts in text, this work integrates both local features (object-level) and global features (pixel-level) from different modalities through image-guided attention, object-guided attention, and hybrid feature attention to solve the problem of sparsity and noise. We then explore the few-shot and zero-shot aspect (attribute-value) extraction in the e-commerce application field. The first work studies the multi-label few-shot learning by leveraging the auxiliary information of anchor (label) and category description based on the prototypical networks, where the hybrid attention helps alleviate ambiguity and capture more informative semantics by calculating both the label-relevant and query-related weights. A dynamic threshold is learned by integrating the semantic information from support and query sets to achieve multi-label inference. The second work explores multi-label zero-shot learning via semi-inductive link prediction of the heterogeneous hypergraph. The heterogeneous hypergraph is built with higher-order relations (generated by the auxiliary information of user behavior data and product inventory data) to capture the complex and interconnected relations between users and the products. | en |
| dc.description.abstractgeneral | Information extraction is the process of automatically extracting structured information from unstructured sources, such as plain text documents, web pages, images, and so on. In this dissertation, we will first focus on general relation extraction, which aims at identifying and classifying semantic relations between entities. For example, given the sentence `Peter was born in Manchester.' in the newspaper, structured information (Peter, place of birth, Manchester) can be extracted. Then, we focus on attribute-value (aspect) extraction in the application field, which aims at extracting attribute-value pairs from product descriptions or images on e-commerce websites. For example, given a product description or image of a handbag, the brand (i.e. brand: Chanel), color (i.e. color: black), and other structured information can be extracted from the product, which provides a better search and recommendation experience for customers. With the advancement of deep learning techniques, machines (models) trained with large quantities of example input data and the corresponding desired output data, can perform automatic information extraction tasks with high accuracy. Such example input data and the corresponding desired output data are also named annotated data. However, across technological innovation and social change, new data (i.e. articles, products, etc.) is being generated continuously. It is difficult, time-consuming, and costly to annotate large quantities of new data for training. In this dissertation, we explore several different methods to help the model achieve good performance with only a few (few-shot learning) or even no labeled data (zero-shot learning) for training. Humans are born with no prior knowledge, but they can still recognize new information based on their existing knowledge by continuously learning. Inspired by how human beings learn new knowledge, we explore different auxiliary information that can benefit few-shot and zero-shot information extraction. We studied the auxiliary information from existing data statistics, knowledge graphs, corresponding images, labels, user behavior data, product inventory data, optical characters, etc. We enable few-shot and zero-shot learning by adding auxiliary information to the training data. For example, we study the data statistics of both labeled and unlabeled data. We use data augmentation and prompts to generate training samples for no labeled data. We utilize graphs to learn general patterns and representations that can potentially transfer to unseen nodes and relations. This dissertation provides the exploration of how utilizing the above different auxiliary information to help improve the performance of information extraction with few annotated or even no annotated training data. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:39733 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119210 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Information Extraction | en |
| dc.subject | Few-Shot Learning | en |
| dc.subject | Zero-Shot Learning | en |
| dc.title | Few-Shot and Zero-Shot Learning for Information Extraction | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Science & Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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