In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts (2020)

Abstract: Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. First, we present the Focused Open Biological Information Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. We then run both the narrow IE system and a state-of-the-art Open IE system on a corpus of 10K open-access scientific biological texts. We show that a significant amount (65%) of erroneous and uninformative Open IE extractions can be filtered using narrow IE extractions. Furthermore, we show that the retained extractions are significantly more often informative to a reader.

## introduction identifying the central theme and concepts in scientific texts is a time-consuming task for experts and a hard task for laymen @xcite @xcite . this problem is even more pronounced in inter-disciplinary fields of study, where experts in a target domain often lack the deeper knowledge of a source domain @xcite . a specific example is biomimetics, an engineering problem-solving process in which one draws on analogous biological solutions @xcite . a major issue is that engineers (target domain) know little biology (source domain) or characteristics of plants or figure 1 : example of semi-open relation extraction in the biology domain @xcite . we first extract the trade-off expressed between the central concepts 'safety' and 'efficiency' (in blue), that takes place specifically in 'conifer species' (in green). we further explore the content of a paper by investigating the results of an open information extraction (oie) system (in red). the central concepts captured by a trade-off mechanism enable the filtering of many irrelevant oie extractions, which are found to be errorprone in scientific texts. further oie extractions found in the document can shed light on the semantic meaning of relevant concepts, e.g., 'xylem', as depicted at the top of the figure (in the red box). recently, trade-off relations have become of interest to biomimetics @xcite because a trade-off defined in technology can be directly used to search for relevant texts in biology @xcite . trade-off relations express a problem space in terms of mutual exclusivity constraints between competing demands. therefore, tradeoffs play a prominent role in evolutionary thinking @xcite and are the principal relation under investigation in a significant portion of biology research papers @xcite . the functional demands that are traded off are usually abstract and domain-independent terms, such as 'safety' and 'efficiency' in figure 1 . a gap remains in quickly comprehending the central information in a text, e.g., the biological mechanisms that are used to manipulate a trade-off. information extraction (ie), and specifically relation extraction (re), can improve the access to central information for downstream tasks @xcite @xcite @xcite . however, the focus of current re systems and datasets is either too narrow, i.e., a handful of semantic relations, such as 'used-for' and 'synonymy', or too broad, i.e., an unbounded number of generic relations extracted from large, heterogeneous corpora @xcite , referred to as open ie (oie) @xcite . narrow approaches to ie from scientific text @xcite @xcite cover only a fraction of the information captured in a paper -usually what is within an abstract. it has been shown that scientific texts contain many unique relation types and, therefore, it is not feasible to create separate narrow ie classifiers for these @xcite . on the other hand, oie systems are primarily developed for the web and news-wire domain and have been shown to perform poorly on scientific texts. what laymen really need is a bit of both: the accuracy of narrow re systems to extract central relations from scientific texts and the flexibility of an oie system to capture a much larger fraction of the possible relations expressed in scientific texts. this work aims to enable rapid comprehension of a large scientific document by identifying a) the central concepts in a text and b) the most significant relations that govern these central concepts. to this end, we introduce the task of semi-open relation extraction (sore); figure 1 illustrates the sore process. first, we find the central concepts 'safety' and 'efficiency' involved in a trade-off relation. then, by using the argument concepts of the relation as anchor points, we can explore further concepts and relations, e.g., 'xylem' in figure 1 . uncovering these relations can elucidate the meaning of unfamiliar concepts to a layperson @xcite . the sore approach is hypothesized to reduce the number of uninformative extractions without limiting re to a finite set of relations, which could generally benefit ie from sci-entific articles, e.g., materials discovery @xcite and drug-gene-mutation interactions @xcite . to address sore we create the focused open biological information extraction (fobie) dataset. fobie includes manually-annotated sentences that express explicit trade-offs, or syntactically similar relations, that capture the central concepts in full-text biology papers. we train a span-based re model used in a strong scientific ie system @xcite to jointly extract these relation structures. we explore sore and use the output of our model to filter the output of an oie system @xcite @xcite on a corpus of biology papers. qualitative analyses show that the output of a narrow re model can speed up expert analysis of trade-offs in biological texts, and be used to filter out both erroneous and uninformative oie extractions. ## conclusions we introduce the task of semi-open relation extraction (sore) on scientific texts and the focused open biological information extraction (fobie) dataset. we adapt off-the-shelf ie systems to show that sore is feasible, and that our approach is worth improving upon -both in terms of performance, as well as reducing the system's complexity. a strong scientific ie system is used as a baseline, and its output is used to filter the relations found by a state-of-the-art oie system. oie from scientific text is a hard task. the large number of errors that we find in oie extractions from scientific texts render them near-useless to downstream computing tasks. a human reader may, nevertheless, find many incorrect extractions informative. an issue for humans is the sheer amount of oie extractions and the high proportion of uninformative extractions. we show that our approach trade-off relations trade-off arguments argument modifiers sleep cognitive abilities energy conservation memory retention (the keeping of memory over prolonged periods of time) memory consolidation (in bats) (without a food reward) (shift from short-to longterm memory) (using torpor) examples filtered oie extractions (a memory; is normally formed; after repeated learning events; sleep enhances this process) (learning; is associated; with a food reward) (sleep deprivation; has; negative effects on both memory consolidation) (torpor; has; a negative influence on memory consolidation) (digestion; prevents; the bats; from falling into torpor quickly) (torpor ; indeed affects ; learning abilities) to sore reduces the number of oie extractions by 65%, while increasing the relative amount of informative extractions by 5.75%. as a result, sore improves the ability for a reader to quickly skim through the remaining extractions, or sentences that they are sourced from, and analyze how central concepts are related in a scientific text. the presented approach is currently limited to the domain of biology and the use of trade-off relations, but we expect that central relations can be identified for other scientific domains that enable sore. we show that creating a dataset for narrow re can be done relatively cheaply by re-annotating the output of a simple rbs. similarly, sore may aid the collection of a dataset for scientific oie.