A P redicate- F unction- A rgument A nnotation of N atural L anguage for O pen- D omain I nformation e X pression (2020)

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 contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly 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 challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.

## introduction in the past decades, various oie (open information extraction) systems @xcite @xcite @xcite have been developed to extract various types of facts. earlier oie systems extract verbal relations between entities, while more recent systems enlarge the types of relations. for example, rel-noun @xcite extract nominal properties. @xcite can extract four types of facts: verbal, prepositional, nominal, and conceptional. ollie @xcite and @xcite extract relations between clauses. in addition to extracting the fact tuples, nestie @xcite and @xcite extract nested facts. furthermore, minie @xcite 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 concerned 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 composition of several extraction strategies (for rule-based systems) or data labeling strategies (for end-to-end supervised learning). it is rather straightforward for specific problems. however, this practice has several major drawbacks outlined as follows: as the oie task has attracted more and more interest @xcite @xcite @xcite , the above mentioned drawbacks have delayed the progress of oie techniques. the key to conquering those obstacles is to provide a shared platform for all oie algorithms, which express all the information in sentences in the form of oie facts (that is, predicate-arguments tuples) without losing information. oie strategies can focus on inferring new facts from existing ones without knowing the existence of the sentence. with this platform, the strategies are reusable and can be fairly compared. when confronting a specific task, one can select a set of strategies or develop new strategies and run the strategies on the platform to build a new oie pipeline. in this manner, the adaptability is much improved. this new methodology of oie is shown in figure 1b . we name the task of implementing such a platform as open information expression (oix), where expression is used to distinguish from extraction to emphasize that it focuses on expressing all the information in the sentence rather than extracting the interested part of the information. this methodology potentially results in a multitask learning scenario where many agents (each one is interested in a part of information) compete with each other for words. this competition may result in more robust expressions than those who only extract part of the information. this paper focuses on investigating the oix task requirements and finding a solution for this task. in section 2, we discuss the principle of design solution for oix and propose a solution -the open information annotation (oia) -to fulfill those principles. the oia of a sentence is a single-rooted directed-acyclic graph (dag) with nodes representing phrases and edges connecting the predicate nodes to their argument nodes. we describe the detailed annotation strategies of oia in section 3. based on the oia, several featured strategies from existing oie algorithms can be ported to work on the oia. section 4 discusses the possible implementation of those strategies on the oia. we label a data set of oia graphs, build a rule-based pipeline for automatically generating oia graphs from sentences, and evaluate the pipeline's performance on the labeled data set. all these work are stated in section 5. we discuss the connection from oia to universal dependency, abstract meaning representation @xcite , and saoke @xcite in section 6. we conclude the paper in section 7. ## information expression using oia in this section, we show how to express information involved in various language phenomenons with our oia. we can only brief the basic framework in the limited content of this paper. more details can be found on the online website for oix foot_0 . ## inference operations on oia graph after the oia graph is constructed, inference operations can be applied to generate a new graph. in this way, strategies from existing oie algorithms can be ported to the oia pipeline. we describe several possible operations as follows: constant merging and expansion. noun phrases with conjunction/dis-conjunction and preposition involved (such as "the deaths of the security guards and police") may correspond to many nodes in the default oia graph, which raise the costs of reading and annotation of the oia graph. we can merge those nodes as one constant node to reduce the cost and expand it back when necessary. figure 2 shows the merged versions of the oia graphs. ## parsing sentence into oia graph this section introduces the automatic pipeline for parsing sentences in english into oia graphs, which is illustrated in figure 4 . we first introduce each component of the pipeline, and then evaluate the proposed oia parser's performance. ## discussion dependency graph. one may wonder whether it is necessary to propose a new oix or oia learning task since the information in oia can also be expressed by the dependency graph, especially enhanced ++. however, the above experiments reveal that even with our very carefully written rule system, the error rate is still high. due to the complexity of the natural language and the error in the dependency pipeline, it is very difficult to improve the rule-based pipeline. on the contrary, based on phrases with much fewer types of edge, the oia is much simpler than the dependency graph, so end-to-end learning may avoid the error introduced by the dependency parser and obtain better results, which belongs to our future work. defining the task and building a rule-based pipeline as the baseline is the first step to learn a good oia annotator. amr. abstract meaning representation (amr) @xcite is a symbolic representation of the sentence. same as our oia, information lossless is also a principle of amr. amr contains approximately 100 relations and selects symbolized concepts from propbank @xcite . it is also very abstract that sentences with the same meaning but in very different expressions will share the same amr annotation. as a result, amr is difficult to label (cost about 10 min to label a sample foot_3 ) and is very difficult to learn. oia can be viewed as an open-domain approximation of amr and maybe a valuable step for amr learning. saoke. saoke(symbol aided open knowledge expression) @xcite is our previous attempt to express various types of knowledge uniformly. it is designed following four requirements: completeness, accurateness, atomicity, and compactness, which are the predecessors of the principles of oix. however, due to the limitation of the annotation form (a list of tuples), the expression capability of saoke is restricted, while the oia greatly extends the expression capability. several end-to-end learning strategies, such as dual learning @xcite and reinforcement learning @xcite are developed to learn the saoke annotation, which can be ported to the learning of oia graphs. ## conclusions and future work this paper proposes a reusable and adaptive pipeline to construct oie systems. as the core of the pipeline, the open-domain information expression (oix) task is thoroughly studied, and an open information annotation (oia) is proposed as a solution to the oix task. we discuss how to port the strategies of various existing oie algorithms to the oia graph. we label data for oia annotation and build a rule-based baseline method to convert sentences into oia graphs. there are many potential directions for future work on oia, including 1) more labeled data; 2) better learning algorithm; 3) becoming crosslingual by adding support for more natural languages; 4) porting existing oie strategies on oia and evaluating the performance compared with the original ones.