Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral Classificationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Comparing human and LLM proofreading in L2 writing: Impact on lexical and syntactic features
This study examines the lexical and syntactic interventions of human and LLM proofreading aimed at improving overall intelligibility in identical second language writings, and evaluates the consistency of outcomes across…
SentenceMulti-Scale Feature and Metric Learning for Relation Extraction
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word seq…
Metric LearningRelationRelation ExtractionDetecting Syntactic Features of Translated Chinese
We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (S…
TranslationSyntax-Aware Language Modeling with Recurrent Neural Networks
Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words. In this paper, we argue this deprives the LM of crucial syntactic signals that can be detected at high confi…
Language ModelingLanguage Modelling