Fan, Y.; Li, B.; Sataer, Y.; Gao, M.; Shi, C.; Cao, S.; Gao, Z. Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences. Appl. Sci.2023, 13, 9412.
Fan, Y.; Li, B.; Sataer, Y.; Gao, M.; Shi, C.; Cao, S.; Gao, Z. Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences. Appl. Sci. 2023, 13, 9412.
Fan, Y.; Li, B.; Sataer, Y.; Gao, M.; Shi, C.; Cao, S.; Gao, Z. Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences. Appl. Sci.2023, 13, 9412.
Fan, Y.; Li, B.; Sataer, Y.; Gao, M.; Shi, C.; Cao, S.; Gao, Z. Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences. Appl. Sci. 2023, 13, 9412.
Abstract
Most natural language processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works address the issue with an intuition of decomposing complex sentences and linking simple ones, such as RST-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple-sentence-decomposition (SSD), etc. However, these works are not applicable for semantic parsing like abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments to semantic relations and unavailabilities to preserve original semantics. Following the same intuition and avoiding deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), based on the linguistic research of clause hierarchy. With the HCA framework, we annotate a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decompose HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more silver annotations.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
