P attern R ank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms (2022)

Abstract: In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers. We propose a semi-supervised graph-based algorithm for pattern acquisition that scores patterns and the relations they extract jointly, using a variant of PageRank. We insert light supervision in the form of seed patterns or relations, and model it with several custom teleportation probabilities that bias random-walk scores of patterns/relations based on their proximity to correct information. We evaluate our approach on Few-Shot TACRED, and show that our method outperforms (or performs competitively with) more expensive and opaque deep neural networks. Lastly, we thoroughly compare our proposed approach with the seminal RlogF pattern acquisition algorithm of, showing that it outperforms it for all the hyper parameters tested, in all settings.

## introduction rule-based methods hastily fell out of favor after the "deep learning tsunami" hit natural language processing @xcite . however, deep learning methods are not perfect: they continue to remain "blackboxes," despite recent effort towards untangling their representations @xcite @xcite . moreover, the presence of various linguistic phenomenons in the hidden representation of deep models does not imply that the model will use them @xcite . on the other hand, rules are interpretable by design. furthermore, changing something in a deep learning model often changes everything (sculley et al., 2015; arpteg 1 code available at https://github. com/clulab/releases/tree/master/ pandl2022-patternrank et al., 2018), while rules are disentangled, i.e., modifying a rule impacts only its own matches. here, we introduce a novel pattern acquisition method for relation extraction, which uses graphbased techniques that operate over the entire topology of the bipartite graph that contains candidate patterns and their extracted relations. more specifically, we leverage a variant of pagerank @xcite to jointly score candidate patterns and their extractions. such an approach has the advantage of softening the sparsity problem: an unknown, but potentially in-domain candidate relation is not automatically considered incorrect (as most "traditional" semi-supervised algorithms would consider it); instead it receives a non-zero score that depends on how reachable it is in this graph. our main contributions are: ## terminology before moving on, we introduce here key terminology used throughout the paper. pattern we define a pattern as a linear sequence of surface and syntactic constraints. for example, the pattern [@xmath0, linked through a nmod_in dependency to a token labeled as a location named entity. ## related work rule-based systems received tremendous attention the "pre deep-learning era." in a seminal work, @xcite proposes a method to learn hyponymy relations using hand-written patterns of the form n p 0 such as {n p 1 .., (and|or)} n p n . @xcite introduced autoslog, a system for automatically learning domain-specific dictionaries using a few hand-written patterns. the system is improved in @xcite by combining the original autoslog with statistical techniques and introduced the rlogf pattern scoring function, defined as: rlogf (pattern i ) = f i n i log 2 (f i ), where f i is the number of extractions for the corresponding class, and n i is the total number of extractions. the duality between patterns and relations has also been explored in @xcite . more concretely, the authors first generate a set of patterns from a set of relations. then, using the previously generated set of patterns they generate a new set of relations. on a high level, we employ a similar algorithm. conceptually similar method has also been in explored in @xcite . ## method our approach, called patternrank, follows an iterative approach, which alternates between learning patterns from known relations (or seeds), and extracting new relations matched by these patterns. the key contribution of our work is the novel scoring strategy, which scores patterns and extracted relations jointly, using a graph-based algorithm. we describe the three components of our method in greater detail below.