Volume 7 Supplement 1
A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
© Tang et al.; licensee Springer. 2015
Published: 19 January 2015
Chemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of chemical entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of chemical entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task.
The 2013 CHEMDNER challenge organizers provided a manually annotated 10,000 UTF8-encoded PubMed abstracts according to a predefined annotation guideline: a training set of 3,500 abstracts, a development set of 3,500 abstracts and a test set of 3,000 abstracts. We developed machine learning-based systems, based on conditional random fields (CRF) and structured support vector machines (SSVM) respectively, for the CEM task for this data set. The effects of three types of word representation (WR) features, generated by Brown clustering, random indexing and skip-gram, on both two machine learning-based systems were also investigated. The performance of our system was evaluated on the test set using scripts provided by the CHEMDNER challenge organizers. Primary evaluation measures were micro Precision, Recall, and F-measure.
Our best system was among the top ranked systems with an official micro F-measure of 85.05%. Fixing a bug caused by inconsistent features marginally improved the performance (micro F-measure of 85.20%) of the system.
The SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features. Each type of the WR feature was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using the all three types of WR features showed better performance than the systems using only one type of the WR feature.
Chemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain, such as detection of drug-protein interactions and adverse drug reactions . Recognizing chemical entities from biomedical literature is a typical named entity recognition (NER) task. Compared with other NER tasks, such as NER in newswire domain , biomedical NER in biomedical domain [3, 4] and clinical NER in clinical domain , there are many unique challenges in chemical entity recognition. For example, a chemical entity may contain a number of long phrases and symbols. There are also a large number of hybrid entities that are partial, systematic and trivial.
A number of comprehensive chemical databases, such as PubChem , ChEBI  Jochem , ChemSpider , MeSH  and DrugBank  have been developed for various purposes, and could potentially be used as lexicons for chemical entity recognition. However, only a very limited number of chemical entity recognition systems have been developed and made publically available, probably due to the lack of large manually annotated corpora. The representative systems are Whatizit , OSCAR3/4 [13, 14] and ChemSpot . Whatizit uses dictionary lookup to recognize chemical entities. OSCAR3/4 are machine learning-based systems that utilize maximum entropy models  on the OSCAR corpus. ChemSpot is a hybrid system that combines a machine learning-based classifier on SCAI corpus [17, 18] with a dictionary. Conditional random fields (CRF)  are used as the classifier in ChemSpot. So far, no comparative evaluation for different chemical entity recognition systems has been investigated on a standard corpus.
To accelerate the development of chemical entity recognition systems, The Spanish National Cancer Research Center (CNIO) and University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER), as a part of BioCreative IV challenge (Track 2) [20–22]. The CHEMDNER challenge includes two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). The subtask 1 is a typical named entity recognition task. The subtask 2 requires participants to rank chemical entities according to their importance in a chemical document. The challenge organizers provided manually annotated abstracts from PubMed (10,000 abstracts), of which 3,500 abstracts were used as a training set, 3,500 abstracts were used as a development set, and 3,000 abstracts were used as a test set.
In this paper, we described our systems for the CEM task. The systems first used a rule-based module for sentence boundary detection and tokenization, and then built machine learning classifiers based on CRF  and structured support vector machines (SSVM)  respectively. Both CRF and SSVM are state-of-the-art machine learning methods for NER, but SSVM has not been applied to chemical entity recognition yet. CRF is a discriminative undirected probabilistic graphical model, while SSVM is a discriminative model based on large margin theory. We also investigated the effects of three types of word representation (WR) features, generated by Brown clustering , random indexing  and skip-gram , on both the CRF-based and SSVM-based systems. WR is a new feature extraction technique that uses unsupervised learning algorithms to generate word-level features from an unlabeled corpus. Those WR features usually contain latent syntactic/semantic information of a word.
Our system was among the top ranked systems with an official micro F-measure of 85.05%. After fixing a bug caused by inconsistent features, the performance was marginally improved with a micro F-measure of 85.20%.
Statistics of the dataset.
Chemical entity recognition
Bag-of-word: unigrams, bigrams and trigrams of tokens in window of [-2, 2].
Orthographical information: word formation information, such as capital letters, numeric characters, and their combinations. All orthographic features used in our system are shown in Table 2.
Orthographic features used in our system.
Morphological information: prefixes/suffixes of lengths from 2 to 5 and word shapes of tokens.
POS: unigrams, bigrams and trigrams of POS (POS) in window of [-2, 2]. Stanford tagger was used for POS tagging http://www-nlp.stanford.edu/software/tagger.shtml.
Document structure information: is current token in a title or not?
Domain knowledge: whether current token includes prefixes/suffixes of chemical compounds and drugs as shown in Table 3 chemical element list and drugs found in UMLS , cTAKES  and MetaMap .
Prefixes/suffixes of chemical compounds and drugs.
alk, meth, eth, prop, but, pent, hex, hept, oct, non, dec, undec, dodec, eifcos, di, tri, tetra, penta, hexa, hepta
ane, ene, yne, yl, ol, al, oic, one, ate, amine, amide
We investigated two machine learning algorithms for chemical entity recognition: CRF and Structural Support Vector Machines (SSVM). CRF is a representative sequence labeling algorithm, which is a discriminative undirected probabilistic graphical model and is suitable for NER tasks. SSVM is a large margin-based discriminative algorithm for structural data, such as sequences, bipartite graph and trees. The SSVM combines the advantages of both CRF and SVM and is also suitable for sequence labeling problems such as NER tasks.
If an entity starts with the end of another one, combine them together to form a new entity.
If an entity only contains numbers and punctuations, remove it.
If there is unmatched ')' in the middle of an entity, combine it with the context from the previous '(' to the start of it to form a new entity.
If there is unmatched '(' in the middle of an entity, combine it with the context from the end of it to the next ')' to form a new entity.
Experiments and evaluation
In this study, we started with a baseline system that adopted features of bag-of-word, orthographic information, morphological information, POS, document structure information and domain knowledge mentioned in the previous section. Then we evaluated the effects of three types of WR features: Brown clustering-based, random indexing-based and skip-gram-based word representations, by adding each of them individually to the baseline systems. Finally, we evaluated the performance of our systems when all three types of WR features were added. All WR features were derived from the entire unlabeled abstracts (27,000) of the 2013 CHEMDNER challenge.
We used CRFsuite http://www.chokkan.org/software/crfsuite/ and SVMhmm http://www.cs.cornell.edu/people/tj/svm_light/svm_hmm.html as implementations of CRF and SSVM respectively. Both of them were trained on both the training and development sets, and their parameters were optimized on the development set when the models were trained on the training set.
where true positives corresponded to chemical entities correctly recognized, false positives corresponded to chemical entities wrongly recognized, and false negatives corresponded to chemical entities not recognized. All of them were calculated using the official evaluation tool provided by the organizers of the CHEMDNER challenge.
The performance of CRF-based and SSVM-based CEM systems when different types of WR features were used.
Baseline + BC
Baseline + RI
Baseline + SKIP
It is not unexpected that the SSVM-based CEM system outperformed the CRF-based CEM system in the current NER task. The same result has been obtained in several
studies on other NER tasks and shown to be due to the higher recall of the SSVM-based systems when same features are used [32–34]. Use of any of the WR features was beneficial to the chemical entity recognition task. When all the three types of WR features were added, both the CRF-based and SSVM-based systems showed better performance than the systems using only one type of WR features. The effects of the WR features to CEM are similar to that reported for biomedical named entity presented in an earlier study . The improvement gains from WR features are mainly due to higher recalls, indicating that WR features can improve the generalization ability of machine learning-based CEM systems.
The machine learning methods used in our systems were based on words (or tokens), which were generated by the preprocessing module shown in Figure 1. To assess the performance of machine learning methods more accurately, we studied the upper boundary performance of the machine learning-based system on the training set after preprocessing. We assigned BIO tags to all words in the training set and converted the words with tags back to chemical entities. We then compared these entities with the gold standard entities using the evaluation program and obtained the following micro precision, recall and F-measure: 97.79%, 97.84% and 97.82%, respectively. It appears that the preprocessing module affects our system to a small extent (about 2.2%). However, a quick perusal of the difference in F-measure between our system and the best system of the CHEMDNER challenge (87.39% vs 85.20%)  showed that it is comparable with the effect of preprocessing module. This observation has led us to determine that improvement of the preprocessing module would be a part of our future area of work or we could change our systems from word-based into char-based to avoid the performance loss caused by tokenization. Further, we also investigated the effect of the post processing module to our system. It improved the F-measure by about 0.2%.
Compared with NER tasks in the newswire domain (F-measure about 90%) , our reported performance on chemical entities was lower, indicating that CEM is more challenging and requires additional investigation and improvement. The possible directions of future pursuits include developing system ensemble approaches and utilizing specific patterns in the chemical domain for features.
In this study, we proposed a machine learning-based system for the CEM subtask of the CHEMDNER challenge, where CRF and SSVM were used as machine learning classifiers. We also investigated the effects of three types of WR features, generated by Brown clustering, random indexing and skip-gram, on both the CRF-based and SSVM-based systems. Our experiments on the CHEMDNER challenge corpus show that the SSVM-based systems outperformed the CRF-based systems when using the same features. Addition of any of the WR features was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using all the three types of WR features showed better performance than the systems only using one type of the WR feature.
This study is supported in part by grants: CPRIT (Cancer Prevention and Research Institute of Texas) R1307, NSFCs (National Natural Science Foundation of China) (61402128, 61173075 and 61272383), Strategic Emerging Industry Development Special Funds of Shenzhen (ZDSY20120613125401420 and JCYJ20120613151940045)and CPSF (China Postdoctoral Science Funding) 2011M500669. We also thank the organizers of the CHEMDNER challenge.
The publication fee of this paper is paid from JCYJ2012061315940045.
This article has been published as part of Journal of Cheminformatics Volume 7 Supplement 1, 2015: Text mining for chemistry and the CHEMNDER track. The full contents of the supplement are available online at http://www.jcheminf.com/supplements/7/S1.
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