Coreference Chains Categorization by Sequence Clustering (2021)

Abstract: The diversity of coreference chains is usually tackled by means of global features (length, types and number of referring expressions, distance between them, etc.). In this paper, we propose a novel approach that provides a description of their composition in terms of sequences of expressions. To this end, we apply sequence analysis techniques to bring out the various strategies for introducing a referent and keeping it active throughout discourse. We discuss a first application of this method to a French written corpus annotated with coreference chains. We obtain clusters that are linguistically coherent and interpretable in terms of reference strategies and we demonstrate the influence of text genre and semantic type of the referent on chain composition.

## introduction coreference chains are discourse structures built upon a set of referential expressions (or mentions) denoting a common discourse entity @xcite . they provide a fundamental mechanism for text interpretation and contribute to cohesion between clauses @xcite . linguistic analysis and automatic detection of coreference chains are still a challenge, due to their complexity and the diversity of their composition @xcite . in particular, a large diversity of linguistic expressions may be used to mention a discourse entity, such as proper nouns, pronouns, possessives, definite or demonstrative noun phrases, in various syntactic positions. this diversity is usually tackled by studying the global characteristics of the coreference chains @xcite , e.g. the number of mentions, their type, the distance between them or by focusing only on the characteristics of the first two mentions. we propose a method that complements these approaches by considering chains as linear sequences of mentions. this makes it possible to identify categories of coreference chains in terms of chaining, which gives new insights for linguistic characterization and provides knowledge about variational dimensions that can help to improve coreference resolution systems @xcite . we resort to sequence analysis techniques which are generally used in social sciences to build typologies of "typical sequences" to study lifecourse trajectories, family histories, professional career paths. in such studies, sequences are used to model the chronology of states or events @xcite . we apply these techniques to coreference chains categorization by considering each mention as a state in the chain chronology, and by characterizing mentions with features traditionally used for studying referring expressions and resolving coreference such as mention types and syntactic functions, relations between mentions, degree of accessibility @xcite @xcite . this paper presents a first step in which only mention types are considered. we apply sequence analysis to a french written corpus annotated with topical chains. we discuss the results in two steps: first we show that the obtained clusters are linguistically interpretable; then we demonstrate that text genre and semantic nature of the referent influence the coreference chain composition. ## results the first application of the clustering method was experimented on the 581 sequences provided by the annodis corpus. each sequence was automatically clustered in three and five classes according to the grammatical category of the mentions. ## discussion and perspectives this application of sequence analysis to coreference chain description provides clusters that are linguistically coherent and interpretable and that reflect different strategies for introducing and maintaining the referent. this method allows for finegrained analyses of the mentions chaining and demonstrate that text genres and the semantic nature of referent influence the chain composition. next steps will consist in refining the model in several ways. first, we will take into account more fine-grained features, for example by distinguishing the different types of pronouns @xcite . this may prove useful to highlight further differences in the composition of chains, potentially also related to the referent types. we will also test other features, such as syntactic functions, relations between mentions, degree of accessibility, coupled with different settings for sequence clustering and a larger variety of referent types (e.g. concrete referent vs abstract referent, generic vs specific, individuals vs collectives, also taking advantage of named entity information); the application of this method to a larger corpus will make it possible to take into account a broader range of text genres and referent types. such a description is a complement to more traditional descriptions and may have longer term applications to coreference resolution, since taking into account variation is crucial to improve the systems @xcite . @xcite show that entitytype information could be a useful feature for coreference resolution. in the same line, @xcite demonstrate that neural models using contextualised representations like bert @xcite improve coreference resolution performances when entity-type features are explicitly taking into account.