Improving chemical disease relation extraction with rich features and weakly labeled data
© The Author(s) 2016
Received: 22 March 2016
Accepted: 28 September 2016
Published: 7 October 2016
Due to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations.
We propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data.
Our text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.
KeywordsChemical-induced disease Relation extraction BioNLP Text mining
Drug/chemical discovery is a complex and time-consuming process that often leads to undesired side effects or toxicity . To reduce risk and the development time, there has been considerable interest in identifying chemical-induced disease (CID) relations between existing chemicals and disease phenotypes by computational methods. Such efforts are important not only for improving chemical safety but also for informing potential relationships between chemicals and pathologies . Much of the knowledge regarding known adverse drug effects or associated chemical-induced disease (CID) relations is buried in the biomedical literature. To make such information available to computational methods, several databases in life sciences such as the Comparative Toxicogenomics Database (CTD) have begun curating important relations manually . However, with limited resources, it is difficult for individual databases to keep up with the rapidly-growing biomedical literature .
During the BioCreative V challenge, a new gold-standard data set was created for system development and evaluation, including manual annotations of chemicals, diseases and their CID relations in 1500 PubMed articles . A large number of international teams participated and achieved the best performance of 57.07 in F-score for the CID relation extraction task. In this work, we aim to improve the best results obtained in the challenge by combining a rich-feature machine learning approach with additional training data obtained without additional annotation cost from existing entries in curated databases. We demonstrate the feasibility of converting the abundant manual annotations in biomedical databases into labeled instances that can be readily used by supervised machine-learning algorithms. Our work therefore joins a few other studies in demonstrating the use of the curated knowledge freely available in biomedical databases for assisting text-mining tasks [17, 46, 48].
More specifically, we formulate the relation extraction task as a classification task on chemical-disease pairs. Our classification model is based on Support Vector Machine (SVM). It uses a set of rich features that combine the advantages of rule-based and statistical methods.
While relation extraction tasks were first tackled using simple methods such as co-occurrence, lately more advanced machine learning systems have been investigated due to the increasing availability of annotated corpora . Typically, the relation extraction task has been considered as a classification problem. For each pair, useful information from NLP tools including part-of-speech taggers, full parsers, and dependency parsers were extracted as features [20, 56]. In the BioCreative V, several machine learning models have been explored for the CID task, including Naïve Bayes , maximum entropy [14, 19], logistic regression , and support vector machine (SVM). In general, the use of SVM has achieved better performance . One of the highest-performing systems was proposed by Xu et al.  with two independent SVM models, sentence-level and document-level classifiers for the CID task. We instead combined the feature vector on both the sentence and document level and developed a unified model. We believe our system is more robust and can be used more easily for other relation extraction tasks with less effort needed for domain adaptation.
SVM-based systems using rich features have been previously studied in biomedical relation extraction [5, 50, 51]. Most useful feature sets include lexical information and various linguistic/semantic parser outputs [1, 2, 15, 23, 38]. Built upon these studies, our rich feature sets include both lexical/syntactic features as previously suggested as well as task specific ones like the CID patterns and domain knowledge as mentioned below.
Although machine learning-based approaches have achieved the highest results, some rule-based and hybrid systems [22, 33] showed highly competitive results during the BioCreative Challenge. In our system, we also integrate the output of a pattern matching subsystem in our feature vector. Thus, our approach can benefit from both machine-learning and rule-based approaches.
To improve the performance, many systems also use external knowledge from both domain specific (e.g., SIDER2, MedDAR, UMLS) and general (e.g. Wikipedia) resources [7, 18, 22, 42]. We incorporate some of these types of knowledge in the feature vector as well.
Another major novelty of this work lies in our creation of additional training data from existing document-level annotations in a curated knowledge base to improve the system performance and to reduce the effort of manual text corpus annotation. Specifically, we make use of previously curated data in CTD as additional training data. Unlike the fully annotated BC5 corpus, these additional training data are weakly labeled: CID relations are linked to the source articles in PubMed (i.e. document-level annotations) but the actual appearances of the disease and chemicals in the relation are not labeled in the article (i.e. mention-level annotations are absent). Hence they are not directly applicable and have to be repurposed when used for training our machine-learning algorithm. Supervised machine-learning approaches require annotated training data which may be difficult to obtain in large scale. To acquire training data, people have recently studied various methods using unlabeled or weakly labeled data [6, 37, 48, 57, 58]. However, such data is often too diverse and noisy to result in high performance . In this paper, we created our labeled data using the idea of distant supervision  but limit the data to be the weakly labeled article that was the source of the curated relation. Thus, this work is most closely related to Ravikumar et al.  with regards to creating training data using existing database curation. However unlike them, we label relations both within and across sentence boundaries and use additionally labeled data only to supplement the gold-standard corpus.
Through benchmarking experiments, we show that our proposed method already achieves favorable results to the best performing teams in the recent BioCreative Challenge when using only the gold-standard human annotations in BC5. Moreover, our system can further improve its performance significantly when incorporating additional training data, by taking advantage of existing database curation at no additional annotation cost.
Statistics of the corpora
Besides the (limited) manual annotation data sets, we created additional training data from existing curated data in the CTD-Pfizer collaboration  where the raw data contains 88,000 articles with document-level annotations of drug-disease and drug-phenotype interactions. To make this corpus consistent with the BC5 corpus, we first filtered those without CID relations in the title/abstracts as some asserted relations are only in the full text. Moreover, the raw data contains no mention-level chemical and disease annotations. Thus, we applied two state-of-the-art bio-entity taggers tmChem  and DNorm  to recognize and normalize chemicals and diseases respectively. To maximize recall, we also applied a dictionary look-up method with a controlled vocabulary (MeSH). As a result, we obtained 18,410 abstracts with 33,224 CID relations and made sure they have no overlap with the BC5 gold standard.
We treat the CID task as a binary classification problem. In the training step, we construct the labeled feature instances from the training set (BC5 training set and CTD-Pfizer corpus). For the BC5 training set, we use the gold-standard entity annotations. For the CTD-Pfizer corpus, we use the recognized chemical and disease mentions as described in previous section. To maximize recall, we also applied a dictionary look-up method with a controlled vocabulary (MeSH). Following name detection, we split the raw text into individual sentences by Stanford sentence splitter , and obtain the parse trees using Charniak–Johnson parser with McClosky’s biomedical model [8, 36]. We then apply the Stanford conversion tool with the “CCProcessed” setting  and the construction method described in Peng et al.  to obtain the extended dependence graph (EDG). In the feature extractor module, for each pair of <Chemical ID, Disease ID> in one document, we iterate through all mention pairs to extract mention-level features. We then merge these mention-level features and add ID-level features to acquire the final feature vector between <Chemical ID, Disease ID>. Finally, Support Vector Machine (SVM) is applied to obtain the model.
In the prediction step, we use the same pipeline to construct the unlabeled feature instances from the BC5 test set, then predict their classes (i.e. whether there is a CID relationship) using the learned model.
In the following subsections, we explain both lexical and knowledge-based features.
The Bag-of-Ngram (BON) features are pairs of consecutive lemma form of words from chemical to disease (or vice versa) when both are in the same sentence. These features (also called N-gram language model features) enrich the BOW feature by word phrases, hence can store the local context. For example, the bag-of-bigram features of Fig. 3 are “(D011899, induce)”, “(induce, acute)” and “(acute, D009395)”. In our system, we use unigrams, bigrams and trigrams. In other words, BON has a sliding window size of 1, 2, and 3 respectively. Please note that we use MeSH IDs instead of actual Chemicals or Diseases in the BON features because MeSH ID is able to differentiate different types of chemicals and diseases thus achieving better results in our experiments.
A common approach to relation extraction involves manually developing rules or patterns, which usually achieves a high precision but is sometimes criticized for its low recall. In our system, we use the output of rule matching as features. It gives the feature vector of four dimensions as its output, each of which corresponds to one trigger in matched patterns: “cause”, “induce”, “associate”, or “produce”.
In this paper, we use the Extended Dependency Graph (EDG) to represent the structure of the sentence . The vertices in an EDG are labeled with information such as the text, part-of-speech, lemma, and named entity, including chemical and diseases. EDG has two types of dependencies: syntactic dependencies and numbered arguments. The syntactic dependencies are obtained by applying Stanford dependencies converter  on a parse tree obtained by the Bllip parser ; the numbered arguments are obtained by investigating the thematic relations described by verbal and nominal predicates. In this paper, we use “arg0” for the agent and “arg1” for other roles such as patient and theme.
arg0 (cause, number)
is-a (sunitinib, inhibitors)
is-a (sorafenib, inhibitors)
arg0 (cause, sunitinib)
arg0 (cause, sorafenib)
EDG is able to unify different syntactic variations in the text, thus only one rule is used in our system to extract CID. “Chemical ← arg0 ← trigger → arg1 → Disease”, where the “trigger” is one of the four words: “cause”, “induce”, “associate”, or “produce”. For each mention pair, the rule-based system will output four Boolean values indicating whether a rule can be applied. We incorporate these four values in the feature vector.
Shortest path features
We also take into account the length of the shortest path by introducing λ length , where 0 < λ ≤ 1 and length is the length of the shortest path. This feature down-weights the contribution of the shortest path exponentially with its lengths. If there are multiple shortest paths between the chemical and disease (in the same sentence or across multiple sentences), we extract all v-walks and e-walks and average λ length . In this paper, we adjust λ to 0.9 based on previous experience [1, 2].
# of chemical mention
# of disease mention
Is chemical in title
Is disease in title
Is chemical in the 1st sentence of the abstract
Is disease in the 1st sentence of the abstract
Is chemical in the last sentence of the abstract
Is disease in the last sentence of the abstract
Are both of chemical and disease in the same sentence
Is disease-chemical relation curated by CTD in the past
Do both disease and chemical exist in the MeSH indexing in the past?
Is any keyword around the disease, such as therapy, complicating, affect, etc.
Is any keyword around the chemical, such as 3.0 mEg/L, mg, etc.
Is “increase” or “decrease” around chemical
Is “increase” or “decrease” around disease
Is “p value” around chemical
Is “p-value” around disease
Is “men”, “women”, or “patient” around chemical
Is “men”, “women”, or “patient” around disease
Results and discussion
Evaluation of named entity results in normalized concept identifiers
Evaluation of CID results
Using text-mined entity mentions
Using gold entity mentions
Avg team results
Best team results
2. Train + dev
3. Train + dev + 1000
4. Train + dev + 5000
5. Train + dev + 10,000
6. Train + dev + 18,410
Contribution of features
Contributions of different features
F-value change (%)
- Shortest path
- #1 ~ #8
- #1 and #2
- #3 and #4
- #5 ~ #8
- #9 ~ #11
- #12 ~ #19
The most significant performance drop occurred when the set of statistical features (−10.69) was removed. In particular, the features checking relation existence in curated databases are quite informative. The second major decrease in performance is due to the removal of EDG with numbered arguments (−1.48 for pattern and −0.51 for shortest path). On the other hand, removing those contextual features #12 ~ #19 from the statistical set did not significantly reduce the performance. It is possible that other features such as BOW, BOB, and shortest path have already captured the context information.
It is also noteworthy that by removing patterns, the precision of the system decreased 2.4 % (from 64.24 to 61.83), while the recall stayed almost the same (0.8 %). This provides support for the usefulness of pattern matching in our system.
Precision on BC5 training set
We show in Table 5 the highest performance of CID relation extraction using the BC5 test set. First, we would like to compare our performance to the inter-annotator agreement (IAA), which generally indicates how difficult the task is for humans and is often regarded as the upper performance ceiling for automatic methods. Unfortunately, the CID relations in the BC5 test set were not double annotated thus the IAA scores by expert annotators are not available for comparison. Alternatively, we compared our performance to the agreement scores from a group of non-experts where IAAs of 64.70 and 58.7 % were obtained respectively, with the use of gold or text-mined entities. As can be seen from Table 5, our system performance of 71.87 and 61.01 % in F-scores compare favorably in both scenarios.
Compared with other relation extraction tasks (such as PPI), we believe CID benefited from two main factors: a) the BioCreative V task provided larger task data which included not only document-level annotations but also mention-level annotations, which are not available in many other similar tasks; and b) the recent advances in disease and chemical named entity recognition and normalization. In fact, the automatic NER and normalization performance for disease and chemicals are approaching human IAAs (F-score in the 80 and 90s, respectively). Unfortunately, this is still not the case for other entities such as gene and proteins.
Comparing the results with and without using gold-standard mentions in the test set (row 6 in Table 5), our results indicate that errors by the named entity tagger bring 10.8 % decrease in F-score for the CID extraction.
Statistics of extraction errors by our method
NER or normalization errors
CID relations mentioned in single sentences
CID relations asserted across sentences
Extracted disease or chemical in CID is too general
The extracted disease/chemical pair is a treatment relation
Annotated CID relations absent in the abstract
Besides NER errors, nearly 35 % of incorrect results were extracted in single sentences. For example, our method failed to extract the CID relation of “renal injury” (MeSH: D058186) and “diclofenac” (MeSH: D004008) from the following sentence: “The renal injury was probably aggravated by the concomitant intake of a non-steroidal anti-inflammatory drug, diclofenac”. Our pattern feature could not be extracted because “aggravate” is not one of our relation trigger words. In addition, the mixture of chemical-induced disease and chemical-treated disease relations within one sentence often poses extra challenges for feature/pattern extraction. Finally, 15 % of total errors were CID relations that are asserted across sentence boundaries, which motivates us to investigate how to capture long-distance CID relations in the future.
In conclusion, this paper discusses a machine-learning based system to successfully extract CID relations from PubMed articles. It may be challenging to directly apply our method to full-length articles (because considerable time may be required to process linguistic analyses) or abbreviated social media text [3, 40]. Another limitation is related to the NER errors: we can expect relation results to increase when mention-level NER results are further improved. In the future, we also plan to investigate the robustness and generalizability of our core approach to other types of important biomedical relations.
YP and ZL conceived the problem. YP implemented methods, performed the experiments, and analyzed the results. CW participated in its design, and analyzed the results. ZL supervised the study. All authors wrote the manuscript. All authors read and approved the final manuscript.
YP is a visiting scholar at National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). He is also a PhD student at University of Delaware. CW is a research fellow at NCBI, NLM, NIH. ZL is Earl Stadtman Investigator at the NCBI where he directs the text mining research and overseas the literature search for PubMed and PMC.
We would like to thank Dr. Robert Leaman for proofreading the manuscript.
The authors declare that they have no competing interests.
This work was supported by the National Institutes of Health Intramural Research Program and National Library of Medicine.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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