Logical Structure Extraction of Electronic Documents Using Contextual Information
by Semere Bitew
Logical document structure extraction refers to the process of coupling the semantic meanings (logical labels) such as title, authors, affiliation, etc., to physical sections in a document. For example, in scientific papers the first paragraph is usually a title. Logical document structure extraction is a challenging natural language processing problem. Elsevier, as one of the biggest scientific publishers in the world, is working on recovering logical structure from article submissions in its project called the Apollo project. The current process in this project requires the involvement of human annotators to make sure logical entities in articles are labelled with correct tags, such as title, abstract, heading, reference-item and so on. This process can be more efficient in producing correct tags and in providing high quality and consistent publishable article papers if it is automated. A lot of research has been done to automatically extract the logical structure of documents. In this thesis, a document is defined as a sequence of paragraphs and recovering the labels for each paragraph yields the logical structure of a document. For this purpose, we proposed a novel approach that combines random forests with conditional random fields (RF-CRFs) and long short-term memory with CRFs (LSTM-CRFs). Two variants of CRFs called linear-chain CRFs (LCRFs) and dynamic CRFs (DCRFs) are used in both of the proposed approaches. These approaches consider the label information of surrounding paragraphs when classifying paragraphs. Three categories of features namely, textual, linguistic and markup features are extracted to build the RF-CRF models. A word embedding is used as an input to build the LSTM-CRF models. Our models were evaluated for extracting reference-items on Elsevier’s Apollo dataset of 146,333 paragraphs. Our results show that LSTM-CRF models trained on the dataset outperform the RF-CRF models and existing approaches. We show that the LSTM component efficiently uses past feature inputs within a paragraph. The CRF component is able to exploit the contextual information using the tag information of surrounding paragraphs. It was observed that the feature categories are complementary. They produce the best performance when all the features are used. On the other hand, this manual feature extraction can be replaced with an LSTM, where no handcrafted features are used, achieving a better performance. Additionally, the inclusion of features generated for the previous and next paragraph as part of the feature vector for classifying the current paragraph improved the performance of all the models.
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