Detecting Diachronic Syntactic Developments in Presence of Bias Terms
Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research. When such results are missing or, as is the case for Vedic Sanskrit, are restricted to small parts of a transmitted corpus, an exploratory framework that detects such changes in a data-driven fashion can substantially support the research process. In this paper, we introduce a customized version of the infinite relational model that groups syntactic constituents based on their structural similarities and their diachronic distributions. We propose a simple way to control for register and intellectual affiliation, and discuss our findings for four syntactic structures in Vedic texts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change
Language change is often assessed against a set of pre-determined time periods in order to be able to trace its diachronic trajectory. This is problematic, since a pre-determined periodization might obscure significant d…
ClusteringDetecting Syntactic Change Using a Neural Part-of-Speech Tagger
We train a diachronic long short-term memory (LSTM) part-of-speech tagger on a large corpus of American English from the 19th, 20th, and 21st centuries. We analyze the tagger's ability to implicitly learn temporal struct…
UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams
Mitigation of Diachronic Bias in Fake News Detection Dataset
Fake news causes significant damage to society.To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific ti…
Fake News DetectionTracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German
We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and eval…
Sentence