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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (2/3): 7-17    DOI: 10.11925/infotech.2096-3467.2021.1066
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Review of Technology Term Recognition Studies Based on Machine Learning
Hu Yamin1,2,Wu Xiaoyan1,Chen Fang1,2()
1Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Department of Library, Information and Archives Management, School of Economics and;Management,University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper reviews the status quo and future directions of technology term recognition studies based on machine learning. [Coverage] We searched “technology term* recognition” in Chinese and English with the Web of Science and CNKI. Then, we expanded our search to include the relevant algorithms literature. A total of 62 representative papers were chosen for this review. [Methods] We summarized the application and differences of machine learning in technology term recognition, and then examined it from four prospects: the classification of algorithms, general procedures, the existing problems, and downstream applications. Finally, we discussed the development trends and future studies. [Results] The algorithms can be divided into single statistical machine learning, single deep learning and hybrid algorithms. The most widely used algorithm is the hybrid method, i.e., the BiLSTM-CRF model. Transfer learning is an important research direction in the future. [Limitations] With the rapid progress of deep learning, hybrid models are constantly emerging, this paper only summarized the popular ones. [Conclusions] There are many issues needs to be addressed. In the future, research on fine-grained entity recognition, feature representation, evaluation and open source toolkits should be strengthened.
Key words： Technology Term Recognition      Machine Learning      Deep Learning     
Received: 22 September 2021      Published: 14 April 2022
ZTFLH:  TP391  
Corresponding Authors: Chen Fang,ORCID：0000-0001-9060-784X     E-mail: chenf@clas.ac.cn
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Yamin Hu
Xiaoyan Wu
Fang Chen

Cite this article:

Hu Yamin, Wu Xiaoyan, Chen Fang. Review of Technology Term Recognition Studies Based on Machine Learning. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 7-17.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1066     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I2/3/7

Differences and Relations Between Technology Terms and Named Entities
模型 生成/判别式 优点 缺点
HMM 生成式模型 实际信息比判别式模型更丰富,单类问题灵活,充分利用了先验知识 输出独立性假设不合理,状态只考虑对应的观察序列;不能利用复杂特征;数据稀疏问题
SVM 判别式模型 小样本分类效果较好 大规模数据、多分类问题效果不佳
CRF 判别式模型 特征设计灵活,考虑了状态之间的关系 训练代价大,复杂度高
Differences of Main Statistical Machine Learning Algorithms
模型 特点 优点 缺点
CNN 用于训练输入层的特征向量 通过卷积核自动提取特征 卷积后末层神经元只得到原始输入数据的少部分信息,无法解决长距离依赖问题,忽略局部和整体的关系
RNN 用于对文本序列进行编码 能够捕获序列单元之间隐藏的关系,即能捕捉长距离依赖关系 序列过长容易梯度消失;存在梯度爆炸问题
LSTM 获取了上文历史信息,使用三种门限机制控制记忆和遗忘 长序列输入效果更好,解决了梯度消失和梯度爆炸问题 不能处理更长的序列;训练时计算费时
BiLSTM 前向传播（历史信息）、后向传播（未来信息） 网络结构的记忆力记住全句的信息 并行计算的利用上不如CNN
Differences of Main Deep Learning Algorithms
模型 特点
CNN-CRF CRF用于提高标注的准确度,CNN用于提取复杂的特征
LSTM-CRF 降低对语料的规范性要求
BiLSTM-CRF 获取双向信息,适用于更复杂的语料
BiLSTM-CNNs-CRF 对BiLSTM-CRF的改进,适用于长句语料的识别
BiLSTM-IDCNN-CRF 对BiLSTM-CRF的改进,IDCNN用于提高训练速度
Att-BiLSTM-CRF 加入注意力机制,突出重点,用于提高识别精度
Differences of Main Hybrid Machine Learning Algorithms
General Process of TTR Using Machine Learning
流程 问题 现有优化途径 改进方向
文本数据获取 数据时滞性、数据规范、数据孤岛与关联 网络关联数据、实时数据库、爬虫获取 实时数据获取
数据预处理 去除噪声 人工去除不包含术语的噪声数据 自动化去除噪声;词汇、句子、篇章不同粒度的研究单元
训练数据处理 标记的质量
标记工作量巨大		 计算不同样本标记一致性、预训练模型;迁移学习、半监督迭代学习 更多的预训练模型;迁移学习的有效性、迭代学习模型的优化验证
训练模型 如何封装、易懂 Python第三方库 如何开发出更简便的开源框架
评估和改良模型 调整参数复杂耗时;如何更精确地实现模型评价,相对优势不清楚、不具体 精确评估、宽松评估等不同评估标准 新的评估方法,具体到模型内部的评估指标
技术术语结果管理 更具体、更广泛的下游研究路径 实体识别+关系抽取的联合模型 细粒度、定制化的实体识别
Existing Problems and Optimization Approaches of General Process of TTR
The Follow-up Research Interests and Applications of TTR
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