Qingcai Chen

Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in F-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been widely used to improve the performance of various natural language processing tasks. However, they have not been used in DNR systems. Compared to the semantic features based on drug dictionaries, the advantage of word embeddings lies in that learning them is unsupervised. In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries. We propose a conditional random fields (CRF)-based system for DNR. The skip-gram model, an unsupervised algorithm, is used to induce word embeddings on about 17.3 GigaByte (GB) unlabeled biomedical texts collected from MEDLINE (National Library of Medicine, Bethesda, MD, USA). The system is evaluated on the drug-drug interaction extraction (DDIExtraction) 2013 corpus. Experimental results show that word embeddings significantly improve the performance of the DNR system and they are competitive with semantic features based on drug dictionaries. F-score is improved by 2.92 percentage points when word embeddings are added into the baseline system. It is comparative with the improvements from semantic features based on drug dictionaries. Furthermore, word embeddings are complementary to the semantic features based on drug dictionaries. When both word embeddings and semantic features based on drug dictionaries are added, the system achieves the best performance with an F-score of 78.37%, which outperforms the best system of the DDIExtraction 2013 challenge by 6.87 percentage points.

Drug Name Recognition: Approaches and Resources

Drug name recognition (DNR), which seeks to recognize drug mentions in unstructured medical texts and classify them into pre-defined categories, is a fundamental task of medical information extraction, and is a key component of many medical relation extraction systems and applications. A large number of efforts have been devoted to DNR, and great progress has been made in DNR in the last several decades. We present here a comprehensive review of studies on DNR from various aspects such as the challenges of DNR, the existing approaches and resources for DNR, and possible directions.

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

BackgroundExtracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.ResultsExperimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.ConclusionsA novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset.

CNN-based ranking for biomedical entity normalization

BackgroundMost state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities.ResultsThe CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance.ConclusionsWe propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

HITSZ_CDR: an end-to-end chemical and disease relation extraction system for BioCreative V

In this article, an end-to-end system was proposed for the challenge task of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction in BioCreative V, where DNER includes disease mention recognition (DMR) and normalization (DN). Evaluation on the challenge corpus showed that our system achieved the highest F1-scores 86.93% on DMR, 84.11% on DN, 43.04% on CID relation extraction, respectively. The F1-score on DMR is higher than our previous one reported by the challenge organizers (86.76%), the highest F1-score of the challenge. Database URL: http://database.oxfordjournals.org/content/2016/baw077

ICRC-DSEDL: A Film Named Entity Discovery and Linking System Based on Knowledge Bases

Named entity discovery and linking are hot topics in text mining, which is very important for text understanding as named entities that usually presented in various formats and some of them are ambiguous. To accelerate the development of related technology, the China Conference on Knowledge Graph and Semantic Computing (CCKS) in 2016 launches a competition, which includes a task on film named entity discovery and linking (i.e., task 1). We participate this competition and develop a system for task 1 of the CCKS competition. The system consists of two individual parts for named entity discovery (NED) and entity linking (EL) respectively. The first part is a hybrid subsystem based on conditional random field (CRF) and structural support vector machine (SSVM) with rich features, and the second part is a ranking subsystem where not only the given knowledge base but also open knowledge bases are used for candidate generation and SVMrank is used for candidate ranking. On the official test dataset of Task1 of CCKS 2016 competition, our system achieves an F1-score of 77.83% on NED, an accuracy of 86.53% on EL and an overall F1-score of 67.35%.

An Initial Ingredient Analysis of Drugs Approved by China Food and Drug Administration

Drug is an important part of medicine. Drug knowledge bases that organize and manage drugs have attracted considerable attention, and have been widely used in human health care in many countries and regions. There are also a large number of electronic drug knowledge bases publicly available. In China, however, there is hardly any publicly available well-structured drug knowledge base, may due to two different types of medicine: Chinese traditional medicine (CTM) and modern medicine (ME). In order to build an electronic knowledge base of drugs approved by China Food and Drug Administration (CFDA), we developed a preliminary ingredient drug analysis system. This system collects all drug names from the website of CFDA, obtains their manuals from three medical websites, extracts the ingredients of drugs, and analyses the distribution of the extracted ingredients. Totally, 12,918 out of 19,490 drug manuals were collected. Evaluation on randomly selected 50 drug manuals shows that the system achieves an F-score of 95.46% on ingredient extraction. According to the distribution of the extraction ingredients, we find that ingredient multiplexing is very common in medicine, especially in herbal medicine, which may provide a clue for drug safety as taking more than one type of drug that contains partially the same ingredients may cause overtaking the same ingredients.