py BART : Evidence-based Syntactic Transformations for IE (2020)

Abstract: Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.

## introduction owing to neural-based advances in parsing technology, nlp researchers and practitioners can now accurately produce syntactically-annotated corpora at scale. however, the use and empirical benefits of the dependency structures themselves remain limited. basic syntactic dependencies encode the functional connections between words but lack many functional and semantic relations that exist between the content words in the sentence. moreover, the use of strictly-syntactic relations results in structural diversity, undermining the efforts to effectively extract coherent semantic information from the resulting structures. thus, human practitioners and applications that "consume" these syntactic trees are required to devote substantial efforts to processing the trees in order to identify and extract the information needed for downstream applications, such as information and relation extraction @xcite . meanwhile, semantic representations @xcite @xcite are harder to predict with sufficient accuracy, calling for a middle ground. indeed, de @xcite introduced collapsed and propagated dependencies, in an attempt to make some semantic-like relations more apparent. the universal dependencies (ud) project 1 similarly embraces the concept of enhanced dependencies @xcite ), adding explicit relations that are otherwise left implicit. @xcite provide further enhancements targeted specifically at english (enhanced ud). 2 @xcite suggest further enhancements to address diathesis alternations. 3 in this work we continue this line of thought, and take it a step further. we present pybart, an 1 universaldepdenencies.org 2 in this paper we do not distinguish between the universal enhanced ud and @xcite 's en-hanced++ english ud. we refer to their union on english as enhanced ud. neo, the one, is state a hero, for chasing this army of robots appos det det det case dobj nmod mark cop nsubj advcl nsubj nsubj nsubj ev xcomp advcl nsubj dobj compound §4.1 §4.1 §4.2 §4.2 §4.3 §4.4 figure 1: representation of neo, the one, is a hero, for chasing this army of robots. the arcs above the sentence are bart additions. the ones below are eud. red arcs are removed in bart while black are retained. to assess the benefits of bart with respect to ud and other enhancements, we compare them in the context of a pattern-based relation extraction task, and demonstrate that bart achieves higher f 1 scores while requiring fewer patterns. the python conversion library, pybart, integrates with the spacy foot_1 library, and is available under an open-source apache license. a web-based demo for experimenting with the converter is also available. https://allenai.github.io/ pybart/ . ## the bart representation we aim to provide a representation that will be useful for downstream nlp tasks, while retaining the following key properties. the proposal has to be (i) based on syntactic structure and (ii) useful for information seeking applications. as a consequence of (ii), we also want it to (iii) make event structure explicit and (iv) allow favoring recall over precision. being based on syntax as the backbone would allow us to capitalize on independent advances in syntactic parsing, and on its relative domain independence. we want our representation to be not only accurate but also useful for information seeking applications. this suggests a concrete methodology ( §2.1) and evaluation criteria ( §5): we choose which relations to focus on based on concrete cases attested in relation extraction and qa-corpora, and evaluate the proposal based on the usefulness in a relation extraction task. in general, information-seeking applications favor making events explicit. current syntactic representations prefer to assign syntactic heads as root predicates, rather than actual eventive verb. in contrast, we aim to center our representation around the main event predicate in the sentence, while indicating event properties such as aspectuality (sam started walking) or evidentiality (sam seems to like them) as modifiers of rather than heads. to do this in a consistent manner, we introduce a new node of type state for copular sentences, making their event structure parallel to those containing finite eventive verbs ( §4.4) finally, downstream users may prefer to favor recall over precision in some cases. to allow for this, we depart from previous efforts that refrain from providing any uncertain information. we chose to explicitly expose some relations which we believe to be useful but judge to be uncertain, while clearly marking their uncertainty in the output. this allows users to experiment with the different cases and assess the reliability of the specific constructions in their own application domain. we introduce two uncertainty marking mechanisms, discussed in §2.3. ## conclusion we propose a syntax-based representation that aims to make the event structure and as many lexical relations as possible explicit, for the benefit of downstream information-seeking applications. we provide a python api that converts ud trees to this representation, and demonstrate its empirical benefits on a relation extraction task. aaron steven white, drew reisinger, keisuke sakaguchi, tim vieira, sheng zhang, rachel rudinger, kyle rawlins, and benjamin van durme. 2016. universal decompositional semantics on universal dependencies. in proceedings of the 2016 conference on empirical methods in natural language processing, pages 1713-1723, austin, texas. association for computational linguistics. dian yu, heng ji, sujian li, and chin-yew lin. 2015. why read if you can scan? trigger scoping strategy for biographical fact extraction. in proceedings of the 2015 conference of the north american chapter of the association for computational linguistics: human language technologies, pages 1203-1208, denver, colorado. association for computational linguistics. yuhao zhang, victor zhong, danqi chen, gabor angeli, and christopher d. manning. 2017. positionaware attention and supervised data improve slot filling. in proceedings of the 2017 conference on empirical methods in natural language processing (emnlp 2017), pages 35-45.