SlowGuess/NAACL_2022_OCR huggingface dataset:
Aryeh Tiktinsky*,1 Vijay Viswanathan*,2 Danna Niezni3 Dana Meron Azagury3 Yosi Shamay3 Hillel Taub-Tabib1 Tom Hope1,4 Yoav Goldberg1,5
$^{1}$ Allen Institute for AI  $^{2}$ Language Technologies Institute, Carnegie Mellon University  $^{3}$ Faculty of Biomedical Engineering, Technion – Israel Institute of Technology  $^{4}$ School of Computer Science & Engineering, The Hebrew University of Jerusalem  $^{5}$ Computer Science Department, Bar Ilan University
aryeht@allenai.org vijayv@andrew.cmu.edu
Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a promising baseline model and identify clear areas for further improvement. We release our dataset, code, and baseline models publicly to encourage the NLP community to participate in this task.
"So far, many monotherapies have been tested, but have been shown to have limited efficacy against COVID-19. By contrast, combinational therapies are emerging as a useful tool to treat SARS-CoV-2 infection." (Ianevski et al., 2021).
Indeed, combining two or more drugs together has proven to be useful for treatments of various medical conditions, including cancer (DeVita et al., 1975; Carew et al., 2008; Shuhendler et al., 2010), AIDS (Bartlett et al., 2006), malaria (Eastman and
Fidock, 2009), tuberculosis (Bhusal et al., 2005), hypertension (Rochlani et al., 2017) and COVID-19 (Ianevski et al., 2020).
In this work, we examine the clinically significant and challenging NLP task of extracting known drug combinations from the scientific literature. We present an expert-annotated dataset and baseline models for this new task. Our dataset contains 1600 manually annotated abstracts, each mentioning between 2 and 15 drugs. 840 of these abstracts describe one or more positive drug combinations, varying in size from 2 to 11 drugs. The remaining 760 abstracts either contain mentions of drugs not used in combination, or discuss combinations of drugs that do not give a combined positive effect.
For the clinical setting, solving the drug combination identification task can help researchers suggest and validate complex treatment plans. For example, when searching for effective treatments for cancer, knowing which drugs interact synergistically with a first line treatment allows researchers to suggest new treatment plans that can subsequently be validated in-vivo and become a standard protocol (Wasserman et al., 2001; Katzir et al., 2019; Ianevski et al., 2020; Niezni et al., 2022).
From an NLP perspective, the drug combination identification task and dataset pushes the boundaries of relation extraction (RE) research, by introducing a relation extraction task with several challenging characteristics:
Variable-length n-ary relations Most work on relation extraction is centered on binary relations (e.g. Li et al. (2016), see full listing in §5), or on  $n$ -ary relations with a fixed  $n$  (e.g. Peng et al. (2017)). In contrast, the drug combination task involves variable-length  $n$ -ary relations: different passages discuss combinations of different numbers of drugs. For each subset of drugs mentioned in a passage, the model must predict if they are used together in a combination therapy and whether this drug combination is effective.
<https://huggingface.co/datasets/allenai/drug-combo-extraction
https://github.com/allenai/drug-combo-extraction
3https://huggingface.co/allenai/ drug-combo-classifier-pubmedbert-dapt
Human Language Technologies, pages 3190 - 3203
Figure 1: Examples of our label scheme. The top example contains two relations: a binary OTHER_COMB relation and a ternary POS_COMB relation. The evidence required to annotate the latter relation is found in a different sentence (highlighted). In the bottom example, each drug is described as a separate treatment rather than a combination therapy.
No type hints As noted by Rosenman et al. (2020) and Sabo et al. (2021), in many relation extraction benchmarks (Han et al., 2018; Sabo et al., 2021; Zhang et al., 2017), the argument types serve as an effective clue. However, argument types do not apply naturally to the drug combination task, in which all possible relation arguments are entities of the same type (drugs) and we need to identify specific subsets of them.
Long range dependencies The information describing the efficacy of a combination is often spread-out across multiple sentences. Indeed, our annotators reported that for  $67\%$  of the instances, the label could not be determined based on a single sentence, requiring reasoning with a larger textual context. Interestingly, our experiments show that our models are not helped by the availability of longer context, showing the limitations of current standard modeling approaches. This suggests our dataset can be a test-bed for models that attempt to incorporate longer context.
Challenging inferences As we show in our qualitative analysis (§4.2), instances in this dataset require processing a range of phenomena, including coordination, numerical reasoning, and world knowledge.
We hope that by releasing this dataset we will encourage NLP researchers to engage in this important clinical task, while also pushing the boundaries of relation extraction.
2 The Drug Combinations Dataset
A set of drugs in a biomedical abstract are classified to one of the following labels:
Positive combination (POS_COMB): the sentence indicates the drugs are used in combination, and the passage suggests that the combination has additive, synergistic, or otherwise beneficial effects which warrant further study.
Non-positive combination (OTHER_COMB): the sentence indicates the drugs are used in combination, but there is no evidence in the passage that the effect is positive (it is either negative or undetermined).<sup>4</sup>
Not a combination (NO_COMB): the sentence does not state that the given drugs are used in combination, even if a combination is indicated somewhere else in the wider context. An example is given in the lower half of Figure 1, where each of the drugs Artesunate and Artemether is given in isolation, and no combination is reported.
Our primary interest is to identify sets of drugs that are positive combinations.
2.1 Relevant Context Size for Classifying Drug Combinations
When formulating the extraction task and designing our data collection methodology, we first analyzed the locality of the phenomenon: to what extent are drug combinations are expressed in a single sentence, or is a larger context is needed? We sampled 275 abstracts that contained known
4We also experimented with another label for combinations that are discouraged (antagonistic, harmful or not effective). The agreement for this label was low, leading us to keep it as a subset of OTHER_COMB.
drug combinations according to DrugComboDB. Analysis showed that  $51\%$  of these abstracts mentioned attempted drug combinations. In  $97\%$  of the abstracts containing drug combinations, all participating drugs in the attempted combination could be located within a single sentence in the abstract (for an example, see the OTHER_COMB relation in Figure 1). However, establishing the efficacy of the combination frequently required a larger context (such as the context accompanying the POS_COMB relation in Figure 1).
2.2 Task Definition
We define each instance in the Drug Combination Extraction (DCE) task to consist of a sentence, drug mentions within the sentence, and an enclosing context (e.g. paragraph or abstract).
The output of the task is a set of relations, each consisting of a set of participating drug spans and a relation label (POS_COMB or OTHER_COMB). Each subset of drug mentions not included in the output set is implicitly considered to have relation label NO_COMB.
More formally, DCE is the task of labeling an instance  $X = \{C,i,D\}$  with a set of relation instances  $R$ , where  $C = (S_{1},\dots S_{n})$  is an ordered list of context sentences (e.g. all the sentences in an abstract or paragraph),  $1\leq i\leq n$  is an index of a target sentence  $S_{i} = (w_{1},\ldots ,w_{n(i)})$  with  $n(i)$  words, and  $D = \{(d_{\mathbf{1start}},d_{\mathbf{1end}}),\dots,(d_{\mathbf{mstart}},d_{\mathbf{mend}})\}$  is a set of  $m > = 2$  spans of drug mentions in  $S$ . The output is a set  $R = \{(c_i,y_i)\}$  where  $c_{i}\in \mathcal{P}(D)$  is a drug combination from  $\mathcal{P}(D)$ , the set of all possible drug combinations, and  $y_{i}\in \{\mathrm{POS\_COMB},\mathrm{OTHER\_COMB}\}$  is a combination label.
2.3 Evaluation Metric
We consider two settings: "Exact Match", a strict version which considers identifying exact drug combinations, and "Partial Match", a more relaxed version which assigns partial credits to correctly identified subsets.
We use standard precision, recall and F1 metrics for both settings. For the partial-match case, we replace the binary 0 or 1 score for a given combination with a refined score: shared_drugs/total_drugs. If there are multiple partial matches with gold relations, we take the
one with maximum overlap. We compute recall as identified Relations/all_goldRelations, and precision as correctRelations/identifiedrelations.
We consider two metrics, the averaged Positive Combination F1 score which compares POS_COMB to the rest, and the averaged Any Combination F1 score which counts correct predictions for any combination label (POS or OTHER) as opposed to NO_COMB. The latter is an easier task, but still valuable for identifying drug combinations irrespective of their efficacy.
2.4 Collecting Data for Annotation
To collect data for annotation we curated a list of 2411 drugs from DrugBank  $^{6}$  and sampled from PubMed a set of sentences which mention 2 or more drugs. Analysis of the first 50 sentences from this sample showed that only  $8/50$  of the sentences included mentions of drug combinations. This meant that annotating the full sample will be costly, and will result in a dataset that's highly skewed toward relatively trivial NO_COMB instances.
We therefore repeated this experiment, sampling sentences whose PubMed abstract included a trigger phrase.  $48\%$  of 50 sampled sentences included mentions of drug combinations. Evaluating the coverage of the trigger list against a new sample of abstracts with known drug combinations showed that  $90\%$  of these new abstracts included one of the trigger words. This suggests our trigger list is useful for fetching label-balanced data, without prohibitively restricting coverage and diversity.
Accordingly, we collected the majority of instances for annotation,  $90\%$ , using a basic search for sentences that contain at least two different drugs and whose abstract contains one of the trigger phrases. To overcome the lexical restrictions imposed by our trigger list, we sample the remaining  $10\%$  of instances using distant supervision: fetch-
$^{6}$ Curation included downloading a premade drug list from DrugBank's website, while removing non pharmacological intervention such as Vitamins and Supplements. The later we got from the FDA orange book.
7Triggers were selected by manually identifying words and phrases which frequently appear in abstracts mentioning drug combinations. These are phrases like "combination", "followed by", "prior to", etc. (see full list in Appendix A.3). The triggers are recall oriented, so while a presence of a trigger increases the chances that an abstract mentions a drug combination, it is definitely not clearly indicative. Importantly, since we're dealing with a wide context, the presence of a trigger in an abstract which includes multiple drugs does not mean the trigger is related to the drugs.
<sup>5</sup>We used Syner&Antag_voting.csv taken from http://drugcombdb.denglab.org/download/ and ranked according to the Voting metric.
Figure 2: Illustration of the data construction process. First we construct the required knowledge resources. Then, we collect data using SPIKE –an extractive search tool– over the PubMed database. The train and test sets were annotated using Prodigy over the curated data. For test data, we collected two annotations for each sample, and then had a domain expert resolve annotation disagreements.
ing sentences containing pairs of drugs known to be synergistic according DrugComboDB, but whose abstract does not include one of our trigger phrases. All data collecting queries were performed using the SPIKE Extractive Search tool (Shlain et al., 2020; Taub-Tabib et al., 2020). The process is illustrated at the top of Figure 2.
2.5 The Annotation Process
Seven graduate students in biomedical engineering took part in the annotation task. The students all completed a course in combination therapies for cancer and were supervised by a principled researcher with expertise in this field.
We provided the participants with annotation guidelines which specified how the annotation process should be carried out (see Appendix A.1) and conducted an initial meeting where we reviewed the guidelines with the group and discussed some of the examples together.
Each of the participants had access to a separate instance of the Prodigy annotation tool (Montani and Honnibal, 2018), pre-loaded with the candidate annotation instances. Once a session starts, the instances (containing of a sentence with marked drug entities, and its context) appear in a sequential manner, with no time limit. For each instance we instructed the annotators to mark all subsets of drugs that participated in a combination, and for each subset to indicate its label (POS_COMB or
Metric	Partial Match	Exact Match
Avg. Any Combination F1	88.9	86.1
Avg. Positive Combination F1	83.4	79.6
Table 1: Agreement scores using our adaptation of F1 score to allow for partial-match.
OTHER_COMB). Moreover, we instructed them to indicate whether the context was needed in order to determine the positive efficacy of the relation.
Despite the considerable time required for expert annotation, we collected annotations for 1634 passages. Among these, 272 were assigned to at least two annotators. After further arbitration by the lead researcher, these were used for the test set. The process is illustrated in the bottom part of Figure 2.
2.6 Inter-annotator Agreement
During the course of the task we calculated inter-annotator agreement multiple times to identify cases of disagreement and provide feedback to annotators. Each time, a set of 25 instances were randomly selected and assigned to all annotators. Agreement was calculated based on a pairwise F1 measure (with some modifications as described in §2.3) and averaged over all pairs of annotators (see discussion of alternative metrics in Appendix A.2).
Final agreement numbers, in Table 1, are satisfactory (Aroyo and Welty, 2013; Araki et al., 2018).
Figure 3: Our baseline architecture, adapted from the PURE model (Zhong and Chen, 2021)
2.7 Resulting Dataset
The dataset consists of 1634 annotated abstracts, split into 1362 train and 272 test instances. These include 1248 relations; 838 are POS_COMB and 410 are OTHER_COMB (with the same label ratio in the train and test sets). 591 sentences contain no drug combination, 877 contain one relation (either POS_COMB or OTHER_COMB), and 166 contain two or more different combinations. Among annotated relations, 900 are binary, 226 are 3-ary, 69 are 4-ary, and 53 are 5-ary or more.
For each instance in the resulting dataset we include the context-required indication provided by the annotators. In 835 out of 1248 relations the annotator marked the context as needed which is  $67\%$  of the time, showing the importance of the context in the DCE task.
3.1 Baseline Model Architecture
We establish a baseline model to measure the difficulty of our dataset and reveal areas for improvement. For our underlying baseline model architecture, we adopt the PURE architecture from Zhong and Chen (2021), which is state-of-the-art on several relation classification benchmarks, including the SciERC binary scientific RE dataset (Luan et al., 2018). The PURE architecture, designed for 2-ary and 3-ary relation extraction, consists of three components. First, special "entity marker" tokens are inserted around all entities in a candidate relation. Next, these marker tokens are encoded
with a contextualized embedding model. Finally, the entity marker embeddings are concatenated and fed to a feedforward layer for prediction.
Unlike the original PURE architecture, we consider the more challenging case of extracting relations of variable arity. To support this setting, we average the entity marker tokens in a relation rather than concatenate. The final baseline model architecture is shown in Figure 3. For the contextual embedding component of this architecture, we experiment with four different pretrained scientific language understanding models (SciBERT (Beltagy et al., 2019), BlueBERT (Peng et al., 2019), Pubmed-BERT (Gu et al., 2020), and BioBERT (Lee et al., 2020)). During training, we only finetune the final *BERT layer. We train each model architecture for 10 epochs on a single NVIDIA Tesla T4 GPU with 15GB of GPU memory, which takes roughly 7 hours to train for each model.
To our knowledge, there are no other models designed for variable-length  $N$ -ary relation extraction, so we consider no other baselines.
3.2 Domain-Adaptive Pretraining
Our baseline model architecture relies heavily on a pretrained contextual embedding model to provide discriminative features to the relation classifier. Gururangan et al. (2020) showed that continued domain-adaptive pretraining almost always leads to significantly improved downstream task performance. Following this paradigm, we performed continued domain-adaptive pretraining ("DAPT") on our contextual embedding models.
We acquired in-domain pretraining data using the same procedure used to collect data for annotation: running a SPIKE query against PubMed to find abstracts containing multiple drug names
This is a similar size to existing human-labeled biomedical relation extraction datasets, such as BioCreative V CDR (Li et al., 2016), which has 1500 abstracts annotated, BioCreative VI (Krallinger et al., 2017), which has 2432 abstracts, and DDI (Herrero-Zazo et al., 2013), which has 714 abstracts.
Model	Positive Combination F1	Any Combination F1
Exact Match	Partial Match	Exact Match	Partial Match
Human-Level	79.6	83.4	86.1	88.9
Rule-based	31.8	45.6	39.1	57.4
SciBERT	44.6 (± 4.6)	55.0 (± 5.9)	50.2 (± 1.9)	63.6 (± 2.7)
w/ DAPT	54.8 (± 3.2)	63.6 (± 2.0)	61.8 (± 2.7)	72.8 (± 2.1)
BlueBERT	41.2 (± 4.8)	51.7 (± 6.0)	47.3 (± 4.2)	59.9 (± 6.2)
w/ DAPT	56.6 (± 2.3)	63.5 (± 3.1)	64.2 (± 2.6)	74.7 (± 2.7)
PubmedBERT	50.7 (± 5.5)	59.6 (± 5.8)	55.9 (± 3.2)	66.7 (± 3.8)
w/ DAPT	61.8 (± 5.1)	67.7 (± 4.8)	69.4 (± 1.7)	77.5 (± 2.2)
BioBERT	45.4 (± 3.7)	55.8 (± 2.2)	46.7 (± 3.6)	58.3 (± 5.1)
w/ DAPT	56.0 (± 6.5)	63.5 (± 7.5)	65.6 (± 1.8)	75.7 (± 2.2)
Table 2: Comparing different foundation models (with and without continued domain-adaptive pretraining) on Exact-Match and Partial-Match relation extraction metrics. Mean score from 4 different random seeds is reported, and standard deviation is computed across seeds.
and a "trigger phrase" (from the list in Appendix A.3). This query resulted in 190K unique abstracts. We do not include any paragraphs from our annotated dataset. We then perform domain-adaptive training against this dataset using the Hugging Face Transformers library. We train for 10 epochs using a learning rate of 5e-4, finetuning all *BERT layers and using the same optimization parameters specified by Gururangan et al. (2020). This pretraining took roughly 8 hours using four 15GB NVIDIA Tesla T4 GPUs.
3.3 Relation Prediction
To apply the model to drug combination extraction, we reduce the RE task to an RC task by considering all subsets of drug combinations in a sentence, treating each one as a separate classification input, and combining the predictions.
This poses two challenges: there may be a large number of candidate relations for a given document, and each relation is classified independently despite the combinatorial structure. To handle these issues, we use a greedy heuristic of choosing the smallest set of disjoint relations whose union covers as many drug entities as possible in the sentence. We do this iteratively: at each step, we choose the largest predicted relation that does not contain any drugs found in the relations chosen at previous iterations.
This greedy heuristic favors large (high arity) relations. Nonetheless, we empirically find this method is helpful for extracting high-precision drug combinations from our model architecture.
3.4 Rule-based baseline
To further validate that the trigger words do not introduce bias to our task, we consider an additional
baseline based on the following rule: if a trigger word is found in the same sentence with multiple drugs, this set of drugs is tagged as POS_COMB.
4.1 Effect of Pretrained LMs and Domain-Adaptive Pretraining
We show results of our baseline model architectures in Table 2. For each model, we report the mean and standard deviation of each metric over four identical models trained with different seeds.<sup>9</sup> Among the four base scientific language understanding models in our experiments, we observe PubmedBERT to be the strongest on every metric. We additionally find that domain-adaptive pretraining provides significantly improvements for every base model, consistently giving 5-10 points of improvement on Positive Combination F1 score. The value of domain-adaptive pretraining supports our observation that encoding domain knowledge is critical to solving this new task.
The rule-based approach underperformed all learned models (30 F1 points under our strongest model, PubMedBERT-DAPT). This shows this task cannot be reduced to keyword identification.
4.2 Qualitative Error Analysis
We identify classes of challenges that make this task difficult, both in terms of human annotation and machine prediction.
Coordination Ambiguity: A known linguistic challenge is the ambiguity that stems from vague coordination. In cases where explicit combination words (e.g. combination, plus, together with, etc)
<sup>9</sup>Seeds used are 2021, 2022, 2023, and 2024
Model	Positive Combination F1	Any Combination F1
Exact Match	Partial Match	Exact Match	Partial Match
No Extra-sentential Context	63.4 (± 0.6)	68.5 (± 1.1)	69.7 (± 1.3)	76.8 (± 1.7)
1 Sentence of Context	63.9 (± 2.3)	69.4 (± 3.5)	71.9 (± 1.1)	78.6 (± 1.8)
2 Sentences of Context	61.9 (± 9.0)	67.6 (± 9.2)	70.1 (± 2.3)	77.9 (± 1.3)
3 Sentences of Context	65.2 (± 2.3)	72.4 (± 1.3)	70.8 (± 1.7)	78.7 (± 1.2)
Table 3: The effect of extra-sentential context on model performance.  $n$  sentences are included on each side of the relation-bearing sentence. Mean and standard deviation of each metric are reported over 4 different random seeds.
are not used, it may be unclear whether two drugs are being used together or separately. For example in “These findings may help clinicians identify patients for whom acamprosate and naltrexone may be most beneficial” it is unclear if acamprosate and naltrexone are being described in combination or as independent treatments, leading to either a POS label for the former or NO_COMB for the latter.
Numerical and Relative Reasoning: In some cases, the effect of a treatment is described in relative or numerical terms, rather than an absolute claim. Consider the example, "The infection rate in the control group was  $3.5\%$  and in the treated group  $0.5\%$ ." Here, the reader must compare the control vs experimental groups and deduce that the experimental outcome is positive, because the treatment yields a lower infection rate.