Automatic Extraction of Clausal Embedding Based on Large-Scale English Text Data

TL;DR

This paper introduces a new method for automatically detecting and annotating embedded clauses in large-scale English text corpora, leveraging constituency parsing and heuristics to analyze naturally-occurring examples.

Contribution

The authors develop a novel extraction tool that identifies embedded clauses in large corpora, providing a large-scale dataset of naturally-occurring embedded clauses for linguistic research.

Findings

High accuracy in detecting embedded clauses on GECS dataset

Successful extraction of embedded clauses from Dolma corpus

Creation of a valuable dataset for linguistic analysis

Abstract

For linguists, embedded clauses have been of special interest because of their intricate distribution of syntactic and semantic features. Yet, current research relies on schematically created language examples to investigate these constructions, missing out on statistical information and naturally-occurring examples that can be gained from large language corpora. Thus, we present a methodological approach for detecting and annotating naturally-occurring examples of English embedded clauses in large-scale text data using constituency parsing and a set of parsing heuristics. Our tool has been evaluated on our dataset Golden Embedded Clause Set (GECS), which includes hand-annotated examples of naturally-occurring English embedded clause sentences. Finally, we present a large-scale dataset of naturally-occurring English embedded clauses which we have extracted from the open-source corpus Dolma using our extraction tool.