BIOMEDICAL WORD SENSE DISAMBIGUATION WITH NEURAL WORD AND CONCEPT EMBEDDINGS

Addressing ambiguity issues is an important step in natural language processing (NLP) pipelines designed for information extraction and knowledge discovery. This problem is also common in biomedicine where NLP applications have become indispensable to exploit latent information from biomedical literature and clinical narratives from electronic medical records. In this thesis, we propose an ensemble model that employs recent advances in neural word embeddings along with knowledge based approaches to build a biomedical word sense disambiguation (WSD) system. Specifically, our system identities the correct sense from a given set of candidates for each ambiguous word when presented in its context (surrounding words). We use the MSH WSD dataset, a well known public dataset consisting of 203 ambiguous terms each with nearly 200 different instances and an average of two candidate senses represented by concepts in the unified medical language system (UMLS). We employ a popular biomedical concept, Our linear time (in terms of number of senses and context length) unsupervised and knowledge based approach improves over the state-of-the-art methods by over 3% in accuracy. A more expensive approach based on the k-nearest neighbor framework improves over prior best results by 5% in accuracy. Our results demonstrate that recent advances in neural dense word vector representations offer excellent potential for solving biomedical WSD

Integrating Relation Constraints with Neural Relation Extractors

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks NNs. However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, ConstraintLoss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction NRE models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.Comment: Accepted to AAAI-202

DWIE: an entity-centric dataset for multi-task document-level information extraction

This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Further, DWIE presented two main challenges when building and evaluating IE models for it. First, the use of traditional mention-level evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can result in measurements dominated by predictions on more frequently mentioned entities. We tackle this issue by proposing a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities. Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting. To realize this, we propose to use graph-based neural message passing techniques between document-level mention spans. Our experiments show an improvement of up to 5.5 F1 percentage points when incorporating neural graph propagation into our joint model. This demonstrates DWIE's potential to stimulate further research in graph neural networks for representation learning in multi-task IE. We make DWIE publicly available at https://github.com/klimzaporojets/DWIE