An improved hidden vector state model approach and its adaptation in extracting protein interaction information from biomedical literature

Large quantity of knowledge, which is important for biological researchers to unveil the mechanism of life, often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and full parsing, have been proposed especially for biomedical applications. In this paper, we present an information extraction system employing a semantic parser using the Hidden Vector State (HVS) model for protein-protein interactions. We found that it performed better than other established statistical methods and achieved 58.3% and 76.8% in recall and precision respectively. Moreover, the pure data-driven HVS model can be easily adapted to other domains, which is rarely mentioned and possessed by other approaches. Experimental results prove that the model trained on one domain can still generate satisfactory results when shifting to another domain with a small amount of adaptation training data

Adversarial Discriminative Domain Adaptation for Extracting Protein-Protein Interactions from Text

Relation extraction is the process of extracting structured information from unstructured text. Recently, neural networks (NNs) have produced state-of-art results in extracting protein-protein interactions (PPIs) from text. While multiple corpora have been created to extract PPIs from text, most methods have shown poor cross-corpora generalization. In other words, models trained on one dataset perform poorly on other datasets for the same task. In the case of PPI, the F1 has been shown to vary by as much as 30% between different datasets. In this work, we utilize adversarial discriminative domain adaptation (ADDA) to improve the generalization between the source and target corpora. Specifically, we introduce a method of unsupervised domain adaptation, where we assume we have no labeled data in the target dataset

A Survey on Recent Named Entity Recognition and Relation Classification Methods with Focus on Few-Shot Learning Approaches

Named entity recognition and relation classification are key stages for extracting information from unstructured text. Several natural language processing applications utilize the two tasks, such as information retrieval, knowledge graph construction and completion, question answering and other domain-specific applications, such as biomedical data mining. We present a survey of recent approaches in the two tasks with focus on few-shot learning approaches. Our work compares the main approaches followed in the two paradigms. Additionally, we report the latest metric scores in the two tasks with a structured analysis that considers the results in the few-shot learning scope

an approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view

Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques.

Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meaningful features from unstructured notes is a challenging task in medical document classification. The domain specific phrases and different synonyms within the medical documents make it hard to analyze them. Analyzing clinical notes becomes more challenging for short documents like abstract texts. All of these can result in poor classification performance, especially when there is a shortage of the clinical data in real life. Two new approaches (an ontology-guided approach and a combined ontology-based with dictionary-based approach) are suggested for augmenting medical data to enrich training data. Three different deep learning approaches are used to evaluate the classification performance of the proposed methods. The obtained results show that the proposed methods improved the classification accuracy in clinical notes classification