Leveraging Linguistic Structure For Open Domain Information Extraction

Abstract

Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns to extract self-contained clauses from longer sentences. We then run natural logic inference over these short clauses to determine the maximally specific arguments for each candidate triple. We show that our approach outperforms a state-of-the-art open IE system on the end-to-end TAC-KBP 2013 Slot Filling task.

Most "babies" are "little" and most "problems" are "huge": Compositional Entailment in Adjective-Nouns

“…we ask "Is this statement that is true of AN also true of N?" While these are not the same question, they are often conflated in NLP, for example, in information extraction, when we use statements about ANs as justification for extracting facts about the head N (Angeli et al, 2015). We focus on the latter question and accept that this prevents us from drawing conclusions about the actual set theoretic relation between the denotation of AN and the denotation of N. However, we are able to draw conclusions about the practical entailment relation between statements about the AN and statements about the N.…”

Relational Summarization for Corpus Analysis

“…Traditionally, relation extraction begins with a fixed notion of what constitutes a desirable "relation" between two arguments, defined by a predefined schema, a syntactic template (Fader et al, 2011), or a collection of seed examples (Angeli et al, 2015). The relation extraction task is then to correctly identify spans in which arguments are joined by a relation.…”

“…We measure the yield of Stanford OpenIE (Angeli et al, 2015) and ClausIE (Del Corro and Gemulla, 2013) on the New York Times and Reddit corpora, and compare each system to our compression-based approach ( §4). 14 We measure these two relation extractors because Stanford OpenIE is included with the popular CoreNLP software and ClausIE achieves the highest recall in two systematic studies of relation extractors (Stanovsky and Dagan, 2016;Zhang et al, 2017).…”

Inducing Event Types and Roles in Reverse: Using Function to Discover Theme

“…Open IE systems (Banko et al, 2007;Angeli et al, 2015) extract general relational patterns between entity pairs, based on domain-independent patterns or heuristics. Similar efforts have emerged to extract more complex event frames by bootstrapping from seed event patterns (Huang and Riloff, 2012;Surdeanu et al, 2006;Yangarber et al, 2000;Patwardhan and Riloff, 2007).…”