CAJ | 학술논문

为实现海量英文学术文本中缩写词及对应缩写定义的识别，本文提出了一种自动缩写识别算法MELearn-AI。该算法在人工标注数据集的基础上，从序列标注的角度，通过最大熵模型实现了计算机领域英文学术文本中的自动缩写识别。MELearn-AI在本文构建的评测数据集“Paren-sen”上得到了95.8%的查准率和86.3%的查全率，相对于其他两组对照实验的效果有较为明显的提升。本文提出的自动缩写识别方法能够在计算机领域的学术文本上取得令人满意的效果，有助于更好地理解并利用该领域术语。
위실현해량영문학술문본중축사사급대응축사정의적식별，본문제출료일충자동축사식별산법MELearn-AI。해산법재인공표주수거집적기출상，종서렬표주적각도，통과최대적모형실현료계산궤영역영문학술문본중적자동축사식별。MELearn-AI재본문구건적평측수거집“Paren-sen”상득도료95.8%적사준솔화86.3%적사전솔，상대우기타량조대조실험적효과유교위명현적제승。본문제출적자동축사식별방법능구재계산궤영역적학술문본상취득령인만의적효과，유조우경호지리해병이용해영역술어。
In order to effectively identify the abbreviations and their corresponding deifnitions from enormous English academic texts, this paper proposes an automatic identification algorithm called MELearn-AI.In the perspective of the sequence labelling,MELearn-AI utilizes a manually labelled dataset and adopts maximum entropy algorithm to train a model, and then identify abbreviations in computer science academic texts based on the model. This method achieves a 95.8% precision rate with a 86.3% recall rate in the "Paren-sen" evaluation dataset created in this paper,it shows an obvious improvement compared to the other two algorithms.This paper proposes a method to identify the abbreviations and their corresponding deifnitions.Tested in English academic texts of computer science, the algorithm achieves satisfactory results, which is helpful to better understanding and adopting the terminology of this ifeld.