A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 2140–2150, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 2140 A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression Mingming Sun, Wenyue Hua, Zoey Liu, Kangjie Zheng, Xin Wang, Ping Li Cognitive Computing Lab Baidu Research No.10 Xibeiwang East Road, Beijing 100193, China 10900 NE 8th St. Bellevue, Washington 98004, USA {sunmingming01, wangxin60, liping11}@baidu.com {norahua1996, zoeyliu0108, kangjie.zheng}@gmail.com Abstract Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX con- tains shared works of OIE algorithms so that OIE strategies can be developed on the plat- form of OIX as inference operations focus- ing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strate- gies to assemble their algorithm for a spe- cific task so that the adaptability may be sig- nificantly increased. This paper focuses on the task of OIX and propose a solution – Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence- OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a chal- lenge owing to the complexity of natural lan- guage sentences, and it is worthy of attracting more attention from the research community. 1 Introduction In the past decades, various OIE (Open Informa- tion Extraction) systems (Banko et al., 2007; Yates et al., 2007; Wu and Weld, 2010; Etzioni et al., 2011; Fader et al., 2011; Mausam et al., 2012) have been developed to extract various types of facts. Earlier OIE systems extract verbal relations between entities, while more recent systems en- large the types of relations. For example, Rel- NOUN (Pal and Mausam, 2016) extract nominal properties. Sun et al. (2018a; 2018b) can extract four types of facts: verbal, prepositional, nominal, and conceptional. OLLIE (Mausam et al., 2012) and ClauseIE (Corro and Gemulla, 2013) extract relations between clauses. In addition to extracting the fact tuples, NestIE (Bhutani et al., 2016) and StuffIE (Prasojo et al., 2018) extract nested facts. Furthermore, MinIE (Gashteovski et al., 2017) add factuality annotations to the facts. Currently, existing OIE systems were typically developed from scratch, generally independent from each other. Each of them has their own con- cerned problem and builds its own pipeline from a sentence to the final set of facts (See Figure 1a). Generally, each OIE system is a complex composi- tion of several extraction strategies (for rule-based systems) or data labeling strategies (for end-to-end supervised learning). …

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