Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies (2023)

Abstract: We propose the use of modal dependency parses (MDPs) aligned with syntactic dependency parse trees as an avenue for the novel task of claim extraction. MDPs provide a document-level structure that links linguistic expression of events to the conceivers responsible for those expressions. By defining the event-conceiver links as claims and using subgraph pattern matching to exploit the complementarity of these modal links and syntactic claim patterns, we outline a method for aggregating and classifying claims, with the potential for supplying a novel perspective on large natural language data sets. Abstracting away from the task of claim extraction, we prototype an interpretable information extraction (IE) paradigm over sentence- and document-level parse structures, framing inference as subgraph matching and learning as subgraph mining. We make our code open-sourced at https://github.com/BBN-E/nlp-graph-pattern-matching-and-mining.

## introduction a promise of natural language processing (nlp) tools is to bring fast understanding of large corpora of unstructured data. this has been achieved through tasks such as summarization @xcite , knowledge base population @xcite , and question-answering systems @xcite , among others. these outcomes provide different views into the chosen data source, highlighting aspects such as event timelines, known and novel relationships between entities, causality, and others. a less explored view of unstructured data may take the form of a claim bank, in which nlp tools provide access to a set of differentiated claims expressed by explicit claimants within a document corpus. we will define a claim as an assertion that is explicitly linked to a source. the source may be a person or an organization. the source may be explicit or implicit; a common example of the latter case is when the author of a document is the source of a claim and is defined as part of the metadata, but is not explicit in the document content. our goal is to automatically identify claims in, and learn claim structures from, natural language text. such claims could then be the impetus for claim verification, clustering, provenance graph generation, etc., as described below. ## linguistic motivation our approach to claim extraction leverages modal dependency parsing (mdp), a document-level annotation scheme for modality introduced by @xcite and adopted by @xcite to crowdsource a dataset and train a neural parser. we use mdp in conjunction with sentence-level syntactic dependency parses. document-level mdp restricts the extraction space to a pool of potential claims that include inter-sentence inner/outer claim relationships. sentence-level dependency parses provide the grammatical structure that allows us to further constrain the pool of potential claims to those that match our analysis of the clausal structure of claims. ## claim extraction via subgraph pattern matching in order to algorithmically exploit this synergy between evidential/modal and clausal/syntactic structure, we explore the task of claim extraction within the framework of subgraph isomorphism. ## building a claim bank we apply our claim extraction algorithm to the crowdsourced english modal dependency dataset @xcite , over which we additionally ran the default stanza dependency parser 5 @xcite (english ewt model) to have both the mdp and dp information available for every document. as a minimal qualitative assessment for the prototyped approach, we build a claim bank by running the subgraph pattern matcher with the ccomp and according to claim patterns over the train, dev and test portions of the crowdsourced dataset with 289, 32 and 32 parsed documents, respectively. we show in as of wednesday, the state had more than 16,460 known cases and 539 known deaths, according to the department. a high-quality claim bank, containing overt claimants linkable to real-world entities, facilitates an exploration of claims by individuals of interest, and provides an avenue for sifting through conflicting perspectives on events. our approach outlines an entirely algorithmic inference procedure to extract knowledge elements (kes), and may therefore be reminiscent of symbolic ai. we have so far discussed how to extract claims with predefined human-curated patterns, but curating such patterns manually is as unreliable and time-consuming as feature engineering, and we want to automatize extracting such patterns from parsed, annotated corpora. we propose subgraph mining as a general-purpose methodology for "learning" patterns, as capable of extracting schematically-defined kes as the parses are expressive of them. the generality and interpretability of the method we formulate below make it as an appealing alternative to task-specific and at times brittle neural extractors, or to powerful but even less interpretable prompt-based approaches. an annotated corpus of claims such as ldc2021e11 or newsclaims consists of kes containing the token indices a of the claimer and b of the inner claim, and labels for the claim topic and claimer stance. we can therefore view the claims corpus as @xmath0, ..., c n } where each claim c i has the structure foot_2 : instead of learning span extractors and classifiers from token-level "surface" annotations, we first apply sentence-and document-level parsers and compose the resulting parses into a digraph as outlined in §4.4. 8 we hypothesize that in the graph composed of available parse types p, features predictive of the kes of interest will be contained in the graph neighborhoods of the annotated tokens. we thus define a neighborhood function that returns the khop portion of the digraph d surrounding given tokens t, where directionality d can be specified to only outgoing (↑) or only incoming (↓) edges from the token node(s), or both (↕). subgraphs created via those neighborhoods are then passed into a subgraph mining procedure ( §6.1) for subgraphmatching-based inference. the learning paradigm is summarized in algorithm 1: a challenge of applying spminer to our nlp domain is that it was developed with molecular graphs in mind that have more variable connectivity patterns than parses, while allowing for at most one label per node or edge (unlike the token nodes in our setting). to mitigate this mismatch, we expand out the graphs so that each attribute is encoded by a synthetic node-edge connection, cf. figures 5 and 6 . the transformation is invertible, such that the expanded graph can be unambiguously collapsed into the original. given the unreliability of sp-miner's default gnn encoder for our graphs, in the search procedure we swap out the neural (batched) subgraph isomorphism with the much slower vf2 algorithm to ensure that the random walk's results are not confounded by the encoder's performance. this substantially slows down our exploration and is a point for future work (cf. §6.3). finally, instead of arbitrary seed nodes, we force spminer to start the growth with the token nodes for the claimer and inner claim, so as to force the resulting motifs to contain the full claim information. ## conclusion we demonstrate the viability of a simple paradigm for extracting and learning ke structures from a variety of parses. we outline avenues to make this approach more efficient and robust, and surmise that as linguistic representations and parsers continue to improve in scope and in accuracy, the nlp community will benefit from interpretable graphbased techniques over them.