Biomedical Coreference Resolution focuses on identifying the coreferences in biomedical texts, which normally consists of two parts: (i) mention detection to identify textual representation of biological entities and (ii) finding their coreference links. Recently, a popular approach to enhance the task is to embed knowledge base into deep neural networks. However, the way in which these methods integrate knowledge leads to the shortcoming that such knowledge may play a larger role in mention detection than coreference resolution. Specifically, they tend to integrate knowledge prior to mention detection, as part of the embeddings. Besides, they primarily focus on mention-dependent knowledge (KBase), i.e., knowledge entities directly related to mentions, while ignores the correlated knowledge (K+) between mentions in the mention-pair. For mentions with significant differences in word form, this may limit their ability to extract potential correlations between those mentions. Thus, this paper develops a novel model to integrate both KBase and K+ entities and achieves the state-of-the-art performance on BioNLP and CRAFT-CR datasets. Empirical studies on mention detection with different length reveals the effectiveness of the KBase entities. The evaluation on cross-sentence and match/mismatch coreference further demonstrate the superiority of the K+ entities in extracting background potential correlation between mentions.

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge.

MOTIVATION: Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events' attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. RESULTS: In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. AVAILABILITY AND IMPLEMENTATION: The source code will be made available at https://github.com/zxy951005/KB-CR upon publication. Data is avaliable at http://2011.bionlp-st.org/ and https://github.com/UCDenver-ccp/CRAFT/releases/tag/v3.1.3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Coreference resolution aims at recognizing different forms in a document which refer to the same entity in the real world. Although many models have been proposed and achieved success, there still exist some challenges. Recent models that use recurrent neural networks to obtain mention representations ignore dependencies between spans and their proceeding distant spans, which will lead to predicted clusters that are locally consistent but globally inconsistent. In addition, these models are trained only by maximizing the marginal likelihood of gold antecedent spans from coreference clusters, which will make some gold mentions undetectable and cause unsatisfactory coreference results. To address these challenges, we propose a neural coreference resolution model. It employs mutual attention to take into account the dependencies between spans and their proceeding spans directly (use attention mechanism to capture global information between spans and their proceeding spans). And our model is trained by jointly optimizing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-2012 English dataset show that our model can detect the most gold mentions and achieve the state-of-the-art coreference performance compared with baselines.