Corpus-Based Task-Specific Relation Discovery (2023)

Abstract: Relation extraction is a crucial language processing task for various downstream applications, including knowledge base completion, question answering, and summarization. Traditional relation-extraction techniques, however, rely on a predefined set of relations and model the extraction as a classification task. Consequently, such closed-world extraction methods are insufficient for inducing novel relations from a corpus. Unsupervised techniques like OpenIE, which extract <head, relation, tail> triples, generate relations that are too general for practical information extraction applications. In this work, we contribute the following: 1) We motivate and introduce a new task, corpus-based task-specific relation discovery. 2) We adapt existing data sources to create Wiki-Art, a novel dataset for task-specific relation discovery. 3) We develop a novel framework for relation discovery using zero-shot entity linking, prompting, and type-specific clustering. Our approach effectively connects unstructured text spans to their shared underlying relations, bridging the data-representation gap and significantly outperforming baselines on both quantitative and qualitative metrics. Our code and data are available in our GitHub repository.

## introduction relation extraction (re) aims to identify semantic relationships between entities in text in order to obtain triples of the @xmath0, relation, tail>, for instance, <washington d.c., capital_of, usa>. re is an important information extraction (ie) technique primarily used to complete knowledge bases (such as yago foot_1 and nell foot_2 ) and construct semantic graphs @xcite . knowledge bases and semantic graphs see wide application in tasks such as questionanswering @xcite , recommendation @xcite , and natural language inference @xcite . traditional relation extraction techniques approach the problem as a multi-class classification problem, and hence assume a predefined set of relations. open information extraction (openie) approaches @xcite seek to remedy this problem by extracting relations from text without a predefined schema. but recent work @xcite has shown that in the absence of a schema, openie results tend to be uninformative or redundant. moreover, openie systems are tuned for high recall and hence extract a very general set of tuples and defer the problem of sifting through the generated triples to find meaningful ones to subsequent analysis. we now introduce the problem of task-specific relation discovery. discovering unseen relations from a corpus serves two functions. firstly, it serves as a starting point to fine-tune relation extraction models on novel relations and unseen domains. secondly, it is intuitively appealing as a data mining task to gather actionable insights from a large unstructured corpus. say, for instance, we are presented with a collection of recent documents reporting on the covid-19 pandemic. discovering all the relationships between viral variants and cities in ohio in the corpus would allow us to detect relations such as "declining in", "spreading in" and "endemic in". this would allow us to automatically identify if there are areas of concern where viral spread is increasing. we define a task as a pair of semantic types (such as humans, geographic locations, sports teams, etc.) between which we are interested in relations. a few concrete examples of this formulation can be seen in figure 1 . in this work, we propose a novel solution to this problem in three steps. first, we identify candidate spans for relation discovery using zero-shot entity linking and typing. then we use a self-supervised prompting technique using an encoder-decoder transformer architecture @xcite to discover relation phrases that describe the relationship between the two entities. finally, we cluster these discovered phrases while keeping the head and tail semantic types of our relations in mind. we briefly summarize our contributions below • we introduce a new task of corpus-based taskspecific relation discovery and modify existing data sources to make available wiki-art, a new dataset for the same • we propose a novel approach to extract candidate sentences, then discover and cluster unseen relations that significantly outperforms our baselines on both qualitative and quantitative metrics. ## related work the three lines of work most relevant to our approach are relation extraction, open information extraction, and prompting. ## problem definition in this section, we formally define corpus-based task-specific relation discovery. a corpus is a collection of documents from any domain. for simplicity, let our corpus @xmath1, s 2 , ..s |s| ] be a sequence of sentences that is, they only correspond to the task outlined using the head and tail semantic types. our end task is to discover the set of relations @xmath2, r 2 , ...r |r| } that occur in t , without assuming any prior knowledge about the relations in t other than the head and tail semantic types. ## methodology our overall architecture is illustrated in figure 2. our procedure for relation discovery comprises three steps that we detail below-1. we identify relevant entities and extract candidate spans of sentences to perform discovery on. 2. we discover the relation phrases that explain the relation between the head and tail entities. 3. we cluster the extracted relation phrases. ## dataset in this section, we briefly describe our dataset wiki-art. we utilize the paragraphs scraped using distant supervision from the rebel dataset @xcite . under the distant supervision setting, a paragraph containing a head and tail entity is assumed to express a relation between two entities if the two entities are related in a background knowledge base (wikidata in our case). head and tail entities are identified using the links from anchor texts. we extract 326 documents (wikipedia abstracts) from the wikipedia pages of movies and comic books. the statistics of our dataset are shown in ## experimental setup we describe our baselines, metrics and the details of the three experimental settings for relation identification, unsupervised relation discovery and corpus-based relation discovery. ## conclusions we formulate a new task of corpus-based taskspecific relation discovery and introduce a new dataset for the same. we empirically demonstrate that existing art are inadequate to tackle this task. to address this, we propose an end-to-end selfsupervised pipeline for relation discovery that significantly outperforms our baselines on both quantitative and qualitative metrics. in the future, we plan on extending our approach to multiple domains in order to identify and correct possible gaps in our methodology.