Semantic relation extraction aware of N-gram features from unstructured biomedical text

An improved author-topic (AT) model with authorship credit allocation schemes

Authorship credit allocation schemes have attracted considerable research attention. However, no consensus about which one is the best has been attained until now, and limited evidence from practical tasks has been reported. Therefore, this study uses the author interest discovery task as a real-world task case to provide valuable insights into authorship credit allocation schemes and guidelines for further practical applications. For this purpose, a novel model, ATcredit, is proposed to strengthen the Author-Topic (AT) model with an authorship credit allocation scheme, and collapsed Gibbs sampling is used to approximate the posterior and estimate model parameters. Extensive experiments using the SynBio dataset reveal several interesting findings as follows. (a) Any scheme for allocating unequal authorship credits performs better than its equal-credit counterpart with our ATcredit model in terms of perplexity. (b) The fixed versions of four out of the six schemes work better than their flexible counterparts with our ATcredit model, regardless of the hyper-authorship strategy. (c) The variation coefficient of credit awards can serve as a criterion to decide whether the hyper-authorship strategy should be used. (d) When the number of authors in a scholarly article is less than three, the six authorship credit allocation schemes are similar to each other with our ATcredit model in terms of perplexity. (e) The harmonic counting scheme performs the best, followed by the arithmetic counting scheme, and the network-based counting scheme performs the worst with our ATcredit model in terms of perplexity. (f) The arithmetic counting scheme is similar to the harmonic counting scheme in terms of the normalised mutual information (NMI) of discovered interests, but the geometric counting scheme is different from the axiomatic and network-based counting schemes.

Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations

Purpose

The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort.

Design/methodology/approach

An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers.

Findings

The collaborative patterns can indeed be leveraged to structure properly every annotator’s behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench.

Originality/value

The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.

Knowledge-Based Biomedical Data Science

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.