로지스틱회귀모형을 통한 언어적 요인들의 유기적 상호작용 분석: 조건추론나무모형과의 비교
An integrated interaction of multiple linguistic factors in logistic regression models: comparison with tree models
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초록
최근 나무모형과 로지스틱회귀분석은 다요인 코퍼스 데이터를 분석하는 데 사용되고 있다. 하지만 나무모형과 상호작용항을 사용하는 로지스틱회귀모형에서는 절의 통사적, 의미적, 화용론적 구조가 서로 유기적으로 연결되어 있기 때문에 발생하는 언어적 요인들의 통합적 효과를 보여주기 어렵다. 본 논문은 필자의 관계사절 유형 분포에 관한 연구(xxxx, 2020)에서 사용되었던 코퍼스 데이터를 활용하여, 언어적 요인들의 통합적 상호작용효과를 분석하기 위해 상수항을 제거하고 변수의 모든 수준의 결합한 새로운 하나의 통합 변수를 만드는 방식의 로지스틱회귀모형을 제안한다.
Abstract
Both tree models and logistic regression models are widely used to analyze multifactorial data in recent corpus studies. By using my previous corpus study on relative clauses, this paper argues that tree models have difficulties in dealing with the integrated effect of multiple linguistic factors, that is, a three-way interaction of non-syntactic factors that affect the preference of relative clause types. In doing so, I show that the integrated or synergistic interaction effect cannot be captured by adding interaction terms in a logistic regression model but by suppressing an intercept and creating a single variable whose levels take on all possible combination of the levels across all three factors. A mixed-effects logistic regression analysis is ultimately implemented by adding the random effect of register, which has been ignored in the corpus linguistics literature on relative clauses.
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