This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/The scientific literature is growing at a rapid rate BIBREF0 . To make sense of this flood of literature, for example, to extract cancer pathways BIBREF1 or find geological features BIBREF2 , increasingly requires the application of natural language processing. Given the diversity of information and its constant flux, the use of unsupervised or distantly supervised techniques are of interest BIBREF3 . In this paper, we investigate one such unsupervised method, namely, Open Information Extraction (OIE) BIBREF4 . OIE is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering BIBREF5 .While OIE has been applied to the scientific literature before BIBREF6 , we have not found a systematic evaluation of OIE as applied to scientific publications. The most recent evaluations of OIE extraction tools BIBREF7 , BIBREF8 have instead looked at the performance of these tools on traditional NLP information sources (i.e. encyclopedic and news-wire text). Indeed, as BIBREF8 noted, there is little work on the evaluation of OIE systems. Thus, the goal of this paper is to evaluate the performance of the state of the art in OIE systems on scientific text.Specifically, we aim to test two hypotheses:Additionally, we seek to gain insight into the value of unsupervised approaches to information extraction and also provide information useful to implementors of these systems. We note that our evaluation differs from existing OIE evaluations in that we use crowd-sourcing annotations instead of expert annotators. This allows for a larger number of annotators to be used. All of our data, annotations and analyses are made openly available.The rest of the paper is organized as follows. We begin with a discussion of existing evaluation approaches and then describe the OIE systems that we evaluated. We then proceed to describe the datasets used in the evaluation and the annotation process that was employed. This is followed by the results of the evaluation including an error analysis. Finally, we conclude.OIE systems analyze sentences and emit relations between one predicate and two or more arguments (e.g. Washington :: was :: president). The arguments and predicates are not fixed to a given domain. (Note, that throughout this paper we use the word `triple” to refer interchangeably to binary relations.) Existing evaluation approaches for OIE systems have primarily taken a ground truth-based approach. Human annotators analyze sentences and determine correct relations to be extracted. Systems are then evaluated with respect to the overlap or similarity of their extractions to the ground truth annotations, allowing the standard metrics of precision and recall to be reported.This seems sensible but is actually problematic because of different but equivalent representations of the information in an article. For example, consider the sentence “The patient was treated with Emtricitabine, Etravirine, and Darunavir”. One possible extraction is:(The patient :: was treated with :: Emtricitabine, Etravirine, and Darunavir)Another possible extraction is:(The patient :: was treated with :: Emtricitabine)(The patient :: was treated with :: Etravirine)(The patient :: was treated with :: Darunavir)Neither of these is wrong, but by choosing one approach or the other a pre-constructed gold set will falsely penalize a system that uses the other approach.From such evaluations and their own cross dataset evaluation, BIBREF8 list the following common errors committed by OIE systems:In our evaluation, we take a different approach. We do not define ground truth relation extractions from the sentences in advance. Instead, we manually judge the correctness of each extraction after the fact. We feel that this is the crux of the information extraction challenge. Is what is being extracted correct or not? This approach enables us to consider many more relations through the use of a crowd-sourced annotation process. Our evaluation approach is similar to the qualitative analysis performed in BIBREF8 and the evaluation performed in BIBREF7 . However, our evaluation is able to use more judges (5 instead of 2) because we apply crowd sourcing. For our labelling instructions, we adapted those used by BIBREF7 to the crowd sourcing setting.As previously noted existing evaluations have also only looked at encyclopedic or newspaper corpora. Several systems (e.g. BIBREF4 , BIBREF9 ) have looked at text from the web as well, however, as far as we know, none have specifically looked at evaluation for scientific and medical text.We evaluate two OIE systems (i.e. extractors). The first, OpenIE 4 BIBREF5 , descends from two popular OIE systems OLLIE BIBREF10 and Reverb BIBREF10 . We view this as a baseline system. The second was MinIE BIBREF7 , which is reported as performing better than OLLIE, ClauseIE BIBREF9 and Stanford OIE BIBREF9 . MinIE focuses on the notion of minimization - producing compact extractions from sentences. In our experience using OIE on scientific text, we have found that these systems often produce overly specific extractions that do not provide the redundancy useful for downstream tasks. Hence, we thought this was a useful package to explore.We note that both OpenIE 4 and MiniIE support relation extractions that go beyond binary tuples, supporting the extraction of n-ary relations. We note that the most recent version of Open IE (version 5) is focused on n-ary relations. For ease of judgement, we focused on binary relations. Additionally, both systems support the detection of negative relations.In terms of settings, we used the off the shelf settings for OpenIE 4. For MinIE, we used their “safe mode" option, which uses slightly more aggressive minimization than the standard setting. In the recent evaluation of MiniIE, this setting performed roughly on par with the default options BIBREF7 . Driver code showing how we ran each system is available.We used two different data sources in our evaluation. The first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 . These sentences were randomly selected by the creators of the dataset. This choice allows for a rough comparison between our results and theirs.The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. This corpus is derived from the 10 most published in disciplines. It includes 11 articles each from the following domains: agriculture, astronomy, biology, chemistry, computer science, earth science, engineering, materials science, math, and medicine. The article text is made freely available and the corpus provides both an XML and a simple text version of each article.We randomly selected 2 sentences with more than two words from each paper using the simple text version of the paper. We maintained the id of the source article and the line number for each sentence.We employed the following annotation process. Each OIE extractor was applied to both datasets with the settings described above. This resulted in the generation of triples for 199 of the 200 WIKI sentences and 206 of the 220 SCI sentences. That is there were some sentences in which no triples were extracted. We discuss later the sentences in which no triples were extracted. In total 2247 triples were extracted.The sentences and their corresponding triples were then divided. Each task contained 10 sentences and all of their unique corresponding triples from a particular OIE systems. Half of the ten sentences were randomly selected from SCI and the other half were randomly selected from WIKI. Crowd workers were asked to mark whether a triple was correct, namely, did the triple reflect the consequence of the sentence. Examples of correct and incorrect triples were provided. Complete labelling instructions and the presentation of the HITS can be found with the dataset. All triples were labelled by at least 5 workers.Note, to ensure the every HIT had 10 sentences, some sentences were duplicated. Furthermore, we did not mandate that all workers complete all HITS.We followed recommended practices for the use of crowd sourcing in linguistics BIBREF11 . We used Amazon Mechanical Turk as a means to present the sentences and their corresponding triples to a crowd for annotation. Within Mechanical Turk tasks are called Human Intelligence Tasks (HITs). To begin, we collected a small set of sentences and triples with known correct answers. We did this by creating a series of internal HITs and loaded them the Mechanical Turk development environment called the Mechanical Turk Sandbox. The HITs were visible to a trusted group of colleagues who were asked to complete the HITs.Having an internal team of workers attempt HITs provides us with two valuable aspects of the eventual production HITs. First, internal users are able to provide feedback related to usability and clarity of the task. They were asked to read the instructions and let us know if there was anything that was unclear. After taking the HITs, they are able to ask questions about anomalies or confusing situations they encounter and allow us to determine if specific types of HITs are either not appropriate for the task or might need further explanation in the instructions. In addition to the internal users direct feedback, we were also able to use the Mechanical Turk Requester functionality to monitor how long (in minutes and seconds) it took each worker to complete each HIT. This would come into factor how we decided on how much to pay each Worker per HIT after they were made available to the public.The second significant outcome from the internal annotations was the generation of a set of `expected' correct triples. Having a this set of annotations is an integral part of two aspects of our crowdsourcing process. First, it allows us to create a qualification HIT. A qualification HIT is a HIT that is made available to the public with the understanding the Workers will be evaluated based on how closely they matched the annotations of the internal annotators. Based upon this, the Workers with the most matches would be invited to work on additional tasks. Second, we are able to add the internal set of triples randomly amongst the other relations we were seeking to have annotated. This allows us to monitor quality of the individual Workers over the course of the project. Note, none of this data was used in the actual evaluation. It was only for the purposes of qualifying Workers.We are sensitive to issues that other researchers have in regards to Mechanical Turk Workers earning fair payment in exchange for their contributions to the HITs BIBREF12 . We used the time estimates from our internal annotation to price the task in order to be above US minimum wage. All workers were qualified before being issued tasks. Overall, we employed 10 crowd workers. On average it took 30 minutes for a worker to complete a HIT. In line with BIBREF13 , we monitored for potential non-performance or spam by looking for long response times and consecutive submitted results. We saw no indicators of low quality responses.In total, 11262 judgements were obtained after running the annotation process. Every triple had at least 5 judgements from different annotators. All judgement data is made available. The proportion of overall agreement between annotators is 0.76 with a standard deviation of 0.25 on whether a triple is consequence of the given sentence. We also calculated inter-annotator agreement statistics. Using Krippendorff's alpha inter-annotator agreement was 0.44. This calculation was performed over all data and annotators as Krippendorff's alpha is designed to account for missing data and work across more than two annotators. Additionally, Fleiss' Kappa and Scott's pi were calculated pairwise between all annotators where there were overlapping ratings (i.e. raters had rated at least one triple in common). The average Fleiss's Kappa was 0.41 and the average of Scott's pi was 0.37. Using BIBREF14 as a guide, we interpret these statistics as suggesting there is moderate agreement between annotators and that agreement is above random chance. This moderate level of agreement is to be expected as the task itself can be difficult and requires judgement from the annotators at the margin.Table 1 shows examples of triples that were associated with higher disagreement between annotators. One can see for example, in the third example, that annotators might be confused by the use of a pronoun (him). Another example is in the last sentence of the table, where one can see that there might be disagreement on whether the subsequent prepositional phrase behind light microscope analysis should be included as part of the extracted triple.We take the variability of judgements into account when using this data to compute the performance of the two extraction tools. Hence, to make assessments as to whether a triple correctly reflects the content from which it is extracted, we rely on the unanimous positive agreement between crowd workers. That is to say that if we have 100% inter-annotator agreement that a triple was correctly extracted we label it as correct.Table 2 show the results for the combinations of systems and data sources. The Correct Triples column contains the number of triples that are labelled as being correct by all annotators. Total Triples are the total number of triples extracted by the given systems over the specified data. Precision is calculated as typical where Correct Triples are treated as true positives. On average, 3.1 triples were extracted per sentence.Figure 1 shows the performance of extractors in terms of precision as inter-annotator agreement decreases. In this figure, we look only at agreement on triples where the majority agree that the triple is correct. Furthermore, to ease comparison, we only consider triples with 5 judgements this excludes 9 triples. We indicate not only the pair-wise inter-annotator agreement but also the number of annotators who have judged a triple to be correct. For example, at the 40% agreement level at least 3 annotators have agreed that a triple is true. The figure separates the results by extractor and by data source.We see that as expected the amount of triples agreed to as correct grows larger as we relax the requirement for agreement. For example, analyzing Open IE's results, at the 100% agreement level we see a precision of 0.56 whereas at the 40% agreement level we see a precision of 0.78. Table 3 shows the total number of correct extractions at the three agreement levels.From the data, we see that extractors perform better on sentences from Wikipedia (0.54 P) than scientific text (0.34 P). Additionally, we see that there is higher annotator agreement on whether triples extracted from Wikipedia and scientific text are correct or incorrect: 0.80 - SD 0.24 (WIKI) vs. 0.72 - SD 0.25 (SCI). A similar difference in agreement is observed when only looking at triples that are considered to be correct by the majority of annotators: 0.87 - SD 0.21 (WIKI) vs. 0.78 - SD 0.25 (SCI) . In both cases, the difference is significant with p-values $<$ 0.01 using Welch's t-test. The differences between data sources are also seen when looking at the individual extraction tools. For instance, for Open IE 4 the precision is 0.19 higher for wikipedia extractions over those from scientific text. With this evidence, we reject our first hypothesis that the performance of these extractors are similar across data sources.We also compare the output of the two extractors. In terms precision, Open IE 4 performs much better across the two datasets (0.56P vs 0.39P). Looking at triples considered to be correct by the majority of annotators, we see that Open IE 4 has higher inter-annotator agreement 0.87 - SD 0.22 (Open IE) vs 0.81 - SD 0.24 (MinIE). Focusing on scientific and medical text (SCI), again where the triples are majority annotated as being correct, Open IE has higher inter-annotator agreement (Open IE: 0.83 - SD 0.24 vs MiniIE: 0.76 - SD 0.25). In both cases, the difference is significant with p-values $<$ 0.01 using Welch's t-test. This leads us to conclude that Open IE produces triples that annotators are more likely to agree as being correct.MinIE provides many more correct extractions than OpenIE 4 (935 more across both datasets). The true recall numbers of the two systems can not be calculated with the data available, but the 40% difference in the numbers of correct extractions is strong evidence that the two systems do not have equivalent behavior.A third indication of differences in their outputs comes from examining the complexity of the extracted relations. Open IE 4 generates longer triples on average (11.5 words) vs. 8.5 words for MinIE across all argument positions. However, Open IE 4 generates shorter relation types than MinIE (Open IE - 3.7 words; MiniIE 6.27 words) and the standard deviation in terms of word length is much more compact for Open IE 4 - 1 word vs 3 words for MinIE. Overall, our conclusion is that Open IE 4 performs better than MinIE both in terms of precision and compactness of relation types, while not matching MinIE's recall, and thus we reject our second hypothesis. The amount of triples extracted from the scientific text is slightly larger than that extracted from the Wikipedia text. This follows from the fact that the scientific sentences are on average roughly 7 words longer than encyclopedic text.The results of our experiment also confirm the notion that an unsupervised approach to extracting relations is important. We have identified 698 unique relation types that are part of triples agreed to be correct by all annotators. This number of relation types is derived from only 400 sentences. While not every relation type is essential for downstream tasks, it is clear that building specific extractors for each relation type in a supervised setting would be difficult.We now look more closely at the various errors that were generated by the two extractors.Table 4 shows the sentences in which neither extractor produced triples. We see 3 distinct groups. The first are phrases that are incomplete sentences usually originating from headings (e.g. Materials and methods). The next group are descriptive headings potentially coming from paper titles or figure captions. We also see a group with more complex prepositional phrases. In general, these errors could be avoided by being more selective of the sentences used for random selection. Additionally, these systems could look at potentially just extracting noun phrases with variable relation types, hence, expressing a cooccurrence relation.We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences. There were several patterns that appeared in these sentences.We also see similar errors to those pointed out by BIBREF8 , namely, uninformative extractions, the difficulty in handling n-ary relations that are latent in the text, difficulties handling negations, and very large argument lengths. In general, these errors together point to several areas for further improvement including:The pace of change in the scientific literature means that interconnections and facts in the form of relations between entities are constantly being created. Open information extraction provides an important tool to keep up with that pace of change. We have provided evidence that unsupervised techniques are needed to be able to deal with the variety of relations present in text. The work presented here provides an independent evaluation of these tools in their use on scientific text. Past evaluations have focused on encyclopedic or news corpora which often have simpler structures. We have shown that existing OIE systems perform worse on scientific and medical content than on general audience content.There are a range of avenues for future work. First, the application of Crowd Truth framework BIBREF15 in the analysis of these results might prove to be useful as we believe that the use of unanimous agreement tends to negatively impact the perceived performance of the OIE tools. Second, we think the application to n-ary relations and a deeper analysis of negative relations would be of interest. To do this kind of evaluation, an important area of future work is the development of guidelines and tasks for more complex analysis of sentences in a crowd sourcing environment. The ability, for example, to indicate argument boundaries or correct sentences can be expected of expert annotators but needs to implemented in a manner that is efficient and easy for the general crowd worker. Third, we would like to expand the evaluation dataset to an even larger numbers of sentences. Lastly, there are a number of core natural language processing components that might be useful for OIE in this setting, for example, the use of syntactic features as suggested by BIBREF16 . Furthermore, we think that coreference is a crucial missing component and we are actively investigating improved coreference resolution for scientific texts.To conclude, we hope that this evaluation provides further insights for implementors of these extraction tools to deal with the complexity of scientific and medical text.

Q: What is the size of the released dataset?
A: 440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.

Q: Were the OpenIE systems more accurate on some scientific disciplines than others?

Q: What is the most common error type?

Q: Which OpenIE systems were used?
A: OpenIE4 and MiniIE

Q: What is the role of crowd-sourcing?