Automatic Extraction of Clausal Embedding Based on Large-Scale English Text Data
This provides linguists with a tool and dataset for studying naturally-occurring embedded clauses, addressing a gap in current research that relies on schematic examples, but it is incremental as it applies existing parsing techniques to new data.
The paper tackled the problem of extracting English embedded clauses from large-scale text data, presenting a method using constituency parsing and heuristics, and resulting in a large-scale dataset extracted from the Dolma corpus, with evaluation on a hand-annotated dataset (GECS).
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.