More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020)

Abstract: Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require “more” from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.

## introduction relational facts organize knowledge of the world in a triplet format. these structured facts act as an import role of human knowledge and are explicitly or implicitly hidden in the text. for example, "steve jobs co-founded apple" indicates the fact (apple inc., founded by, steve jobs), and we can also infer the fact (usa, contains, new york) from "hamilton made its debut in new york, usa". as these structured facts could benefit downstream applications, e.g, knowledge graph completion @xcite , search engine @xcite and question answering @xcite , many efforts have been devoted to researching relation extraction @xcite , which aims at extracting relational facts from plain text. more specifically, after identifying entity mentions (e.g., usa and new york) in text, the main goal of re is to classify relations (e.g., contains) between these entity mentions from their context. the pioneering explorations of re lie in statistical approaches, such as pattern mining @xcite , feature-based methods @xcite and graphical models @xcite . recently, with the development of deep learning, neural models have been widely adopted for re @xcite and achieved superior results. these re methods have bridged the gap between unstructured text and structured knowledge, and shown their effectiveness on several public benchmarks. despite the success of existing re methods, most of them still work in a simplified setting. these methods mainly focus on training models with large amounts of human annotations to classify two given entities within one sentence into pre-defined relations. however, the real world is much more complicated than this simple setting: (1) collecting high-quality human annotations is expensive and time-consuming, (2) many long-tail relations cannot provide large amounts of training examples, (3) most facts are expressed by long context consisting of multiple sentences, and moreover (4) using a pre-defined set to cover those relations with open-ended growth is difficult. hence, to build an effective and robust re system for real-world deployment, there are still some more complex scenarios to be further investigated. in this paper, we review existing re methods (section 2) as well as latest re explorations (section 3) targeting more complex re scenarios. those feasible approaches leading to better re abilities still require further efforts, and here we summarize them into four directions: besides the introduction of promising directions, we also point out two key challenges for existing methods: (1) learning from text or names (section 4.1) and (2) datasets towards special interests (section 4.2). we hope that all these contents could encourage the community to make further exploration and breakthrough towards better re. ## background and existing work information extraction (ie) aims at extracting structural information from unstructured text, which is an important field in natural language processing (nlp). relation extraction (re), as an important task in ie, particularly focuses on extracting relations between entities. a complete relation extraction system consists of a named entity recognizer to identify named entities (e.g., people, organizations, locations) from text, an entity linker to link enti- tim cook is apple's current ceo. 0.05 0.01 0.89 . . . founder place of birth ceo figure 1: an example of re. given two entities and one sentence mentioning them, re models classify the relation between them within a pre-defined relation set. in this section, we introduce the development of re methods following the typical supervised setting, from early pattern-based methods, statistical approaches, to recent neural models. ## "more" directions for re although the above-mentioned nre models have achieved superior results on benchmarks, they are still far from solving the problem of re. most of these models utilize abundant human annotations and just aim at extracting pre-defined relations within single sentences. hence, it is hard for them to work well in complex cases. in fact, there have been various works exploring feasible approaches that lead to better re abilities on realworld scenarios. in this section, we summarize these exploratory efforts into four directions, and give our review and outlook about these directions. ## other challenges in this section, we analyze two key challenges faced by re models, address them with experiments and show their significance in the research and development of re systems foot_2 . ## conclusion in this paper, we give a comprehensive and detailed review on the development of relation extraction models, generalize four promising directions leading to more powerful re systems (utilizing more data, performing more efficient learning, handling more complicated context and orienting more open domains), and further investigate two key challenges faced by existing re models. we thoroughly survey the previous re literature as well as supporting our points with statistics and experiments. through this paper, we hope to demonstrate the progress and problems in existing re research and encourage more efforts in this area.