Shallow Discourse Parsing for Open Information Extraction and Text Simplification (2022)

Abstract: We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, sentences are converted into a hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. As opposed to previously proposed sentence splitting approaches, which commonly do not take into account discourse-level aspects, our TS approach preserves the semantic relationship of the decomposed constituents in the output. A comparative analysis with the annotations contained in RST-DT shows that we capture the contextual hierarchy between the split sentences with a precision of 89% and reach an average precision of 69% for the classification of the rhetorical relations that hold between them. Moreover, an integration into state-of-the-art Open Information Extraction (IE) systems reveals that when applying our TS approach as a pre-processing step, the generated relational tuples are enriched with additional meta information, resulting in a novel lightweight semantic representation for the task of Open IE.

## introduction sentences that present a complex structure can be hard to comprehend by human readers, as well as difficult to analyze by semantic applications @xcite . identifying grammatical complexities in a sentence and transforming them into simpler structures is the goal of syntactic ts. the most relevant method that is used to perform this rewriting step is sentence splitting: it divides a sentence into several shorter components with each of them presenting a more regular syntax that is easier to process by both humans @xcite and machines @xcite . we propose a sentence splitting approach that can be used as a preprocessing step to generate an intermediate representation. the objective is to facilitate and improve the performance of downstream tasks whose predictive quality deteriorates with sentence length and complexity (e.g., see @xcite ; saha and @xcite ; @xcite ; štajner and popović (2018)). our approach aims to break down a complex sentence into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with a simple and regular structure. each of them presents a minimal unit of coherent information and, consequently, cannot be further decomposed into meaningful propositions. however, any sound and coherent text is not simply a loose arrangement of self-contained units, but rather a logical structure of utterances that are semantically connected @xcite . consequently, when carrying out syntactic ts operations without considering discourse implications, the rewriting may easily result in a disconnected sequence of simplified sentences, making the text harder to interpret. the vast majority of existing structural ts approaches though do not take into account discourse-level aspects. therefore, they are prone to producing a set of incoherent utterances where important contextual information is lost. thus, to preserve the coherence structure of the input we propose a context-preserving ts approach. it establishes a semantic hierarchy between the split components by (1) setting up a contextual hierarchy and (2) classifying the semantic relationship that holds between them (see figure 1 ). to the best of our knowledge, this is the first time that syntactically complex sentences are split and rephrased within the semantic context in which they occur. our framework differs from previously proposed approaches by using a linguistically grounded transformation stage that applies clausal and phrasal disembedding mechanisms to contrast (a) condition (c) background (d) list (e) the usage of barium can lead to increased post operative complications. the usage of barium can impede surgical revision. core core caution with non water soluble contrast is mandatory. core context volvulus is suspected. context core elaboration (b) a fluoroscopic study is known as an upper gastrointestinal series. a fluoroscopic study is typically the next step in management. figure 1 : a complex sentence ("a fluoroscopic study which is known as an upper gastrointestinal series is typically the next step in management, although if volvulus is suspected, caution with non water soluble contrast is mandatory as the usage of barium can impede surgical revision and lead to increased post operative complications.") is transformed into a semantic hierarchy of simplified sentences in the form of minimal, self-contained propositions that are linked to each other via rhetorical relations. the output presents a regular, fine-grained structure that preserves the context of the input in the form of hierarchically ordered and semantically interconnected sentences. the hierarchical order and the semantic relationship that hold between the split sentences. (v) we show that the semantic hierarchy can be leveraged to extract relational tuples within their semantic context, resulting in a novel lightweight semantic representation for complex text data in the form of normalized and context-preserving tuples. ## discourse-aware sentence splitting we present dissim, a discourse-aware ts approach that creates a semantic hierarchy of simplified sentences. foot_0 it takes a sentence as input and performs a recursive transformation stage that is based upon a small set of 35 hand-crafted rules. ## a lightweight semantic representation for open ie the fine-grained representation of complex sentences in the form of hierarchically ordered and semantically interconnected propositions may serve as an intermediate representation for downstream tasks. an application area that may benefit greatly from our approach as a preprocessing step is the task of open ie @xcite . we thus assessed the merits of our proposed discourse-aware ts approach in supporting the extraction of relational tuples from complex assertions in downstream open ie applications, demonstrating that the semantic hierarchy of minimal propositions benefits them in two dimensions: in that way, the shallow semantic representation of state-of-the-art open ie systems is transformed into a canonical context-preserving representation of relational tuples. ## related work discourse-level ts the vast majority of structural ts approaches do not take into account discourse-level aspects. however, two notable exceptions have to be mentioned. @xcite was the first to use discourse-aware cues in the simplification process. as opposed to our approach, though, where a semantic relationship is established for each simplified output sentence, only a comparatively low number of sentences is linked by such cue words. another approach that operates on the level of discourse was proposed by @xcite . it performs a semantically motivated content reduction by maintaining only those parts of a sentence that belong to factual event mentions. our approach, on the contrary, aims to preserve all the information contained in the source. discourse parsing the challenge of uncovering coherence structures in texts is pursued in the field of discourse parsing. it aims to identify discourse relations that hold between textual units in a document @xcite . a well-established theory of text structure used in this area is rst. here, textual coherence is explained by the existence of rhetorical relations that hold between adjacent text spans in a hierarchical structure. approaches to detect rhetorical structure arrangements in texts range from early rule-based approaches @xcite to supervised data-driven models that were trained on annotated corpora such as the rst-dt @xcite @xcite . 16 ## conclusion we presented a context-preserving ts approach that transforms structurally complex sentences into a hierarchical representation in the form of core sentences and accompanying contexts that are semantically linked by rhetorical relations. in our experiments, we mapped the simplified sentences from our reference implementation dissim to the edus from rst-dt and showed that we obtain a very high precision of 89% for the constituency type classification and a decent score of 69% on average for the rhetorical relation identification. in the future, we plan to improve the latter step by extending our approach to also capture implicit relationships between the decomposed sentences. christina niklaus, matthias cetto, andré freitas, and siegfried handschuh. 2019b. transforming complex sentences into a semantic hierarchy. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 3415-3427, florence, italy. association for computational linguistics. swarnadeep saha and mausam. 2018. open information extraction from conjunctive sentences. in proceedings of the 27th international conference on computational linguistics, pages 2288-2299, santa fe, new mexico, usa. association for computational linguistics. advaith siddharthan. 2002. an architecture for a text simplification system. in language engineering conference, 2002. proceedings, pages 64-71. ieee. advaith siddharthan. 2006. syntactic simplification and text cohesion. research on language and computation, 4(1):77-109. advaith siddharthan. 2014. a survey of research on text simplification. itl-international journal of applied linguistics, 165(2):259-298. advaith siddharthan and angrosh mandya. 2014. hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules. in proceedings of the 14th conference of the european chapter of the association for computational linguistics, pages 722-731, gothenburg, sweden. association for computational linguistics. radu soricut and daniel marcu. 2003. sentence level discourse parsing using syntactic and lexical information. in proceedings of the 2003 human language technology conference of the north american chapter of the association for computational linguistics, pages 228-235. sanja štajner and goran glavaš. 2017. leveraging event-based semantics for automated text simplification. expert systems with applications, 82:383-395. sanja štajner and maja popović. 2018. improving machine translation of english relative clauses with automatic text simplification. in proceedings of the 1st workshop on automatic text adaptation (ata), pages 39-48, tilburg, the netherlands. association for computational linguistics. gabriel stanovsky and ido dagan. 2016. creating a large benchmark for open information extraction. in proceedings of the 2016 conference on empirical methods in natural language processing, pages 2300-2305, austin, texas. association for computational linguistics. gabriel stanovsky, julian michael, luke zettlemoyer, and ido dagan. 2018. supervised open information extraction. in proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long papers), pages 885-895, new orleans, louisiana. association for computational linguistics. maite taboada and debopam das. 2013. annotation upon annotation: adding signalling information to a corpus of discourse relations. d&d, 4(2):249-281. yizhong wang, sujian li, and houfeng wang. 2017. a two-stage parsing method for text-level discourse analysis. in proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers), pages 184-188, vancouver, canada. association for computational linguistics.