Term Extraction from Chinese Texts Without Word Segmentation

计算机科学 自然语言处理 判决 人工智能 分割 领域(数学分析) 排名(信息检索) 对比度(视觉) 词(群论) 精确性和召回率 期限(时间) 文本分割 符号(正式) 信息抽取 集合(抽象数据类型) 情报检索 模式识别(心理学) 数学 数学分析 物理 程序设计语言 量子力学 几何学
作者
Chuqiao Yu,Ma Pengyu,I.A. Bessmertny,А.В. Платонов,E.A. Poleschuk
标识
DOI:10.1109/icaict.2017.8687047
摘要

The paper is dedicated to the problem of automatic term extraction from natural language texts. One of the first steps in this topic is building a domain thesaurus. Well approved methods of terms extraction based on word frequencies exist for alphabetic languages. Direct application of these methods for hieroglyphic texts is challenged because of missing spaces between words. The sentence segmentation task in hieroglyphic languages is usually solved by dictionaries or by statistical methods, particularly, by means of a mutual information approach. Sentence segmentation methods, as well as methods of terms extraction, separately, do not reach 100 percent precision and recall, and their combination just increases the number of errors. The aim of this work is to improve recall and precision of domain terms extraction from hieroglyphic texts. The proposed method is to identify repetitions of the two, three or four symbol sequences in each sentence and correlation of occurrence frequencies for these sequences in the target domain and contrast documents collection. According to the research, it was stated that a trivial ranking of all possible symbol sequences allows extracting only frequently used terms. Filtering of symbol sequences by their ratio of frequencies in the domain and in the contrast collection gave the possibility to extract reliably frequently used terms and to find satisfactory rare domain terms. Some results of terms extraction for the “Geology” domain from a Chinese text are presented in this paper. A set of articles from the newspaper “Renmin Ribao” was used as a contrast collection and some satisfactory results were obtained.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
、、、完成签到,获得积分10
2秒前
lrq完成签到,获得积分10
4秒前
古月发布了新的文献求助10
4秒前
5秒前
五六七发布了新的文献求助10
7秒前
7秒前
css1997完成签到 ,获得积分10
9秒前
无限飞丹完成签到,获得积分10
9秒前
小饭完成签到,获得积分10
9秒前
Cl1audia发布了新的文献求助10
10秒前
所所应助木可采纳,获得10
10秒前
Gyro完成签到,获得积分10
12秒前
隐形曼青应助lzx采纳,获得10
12秒前
13秒前
体贴紫完成签到,获得积分10
14秒前
14秒前
14秒前
15秒前
五六七完成签到,获得积分10
15秒前
15秒前
liz_发布了新的文献求助10
16秒前
新xin发布了新的文献求助10
17秒前
CodeCraft应助111111111采纳,获得10
18秒前
汉堡包应助秋天里的水采纳,获得10
19秒前
量子星尘发布了新的文献求助10
19秒前
Gyro发布了新的文献求助50
19秒前
慕青应助keyun采纳,获得10
19秒前
体贴紫发布了新的文献求助10
19秒前
20秒前
Dr.Who发布了新的文献求助10
21秒前
22秒前
深情安青应助科研通管家采纳,获得10
22秒前
彭于晏应助科研通管家采纳,获得10
22秒前
搜集达人应助科研通管家采纳,获得10
22秒前
顾矜应助科研通管家采纳,获得10
22秒前
华仔应助科研通管家采纳,获得10
22秒前
Owen应助科研通管家采纳,获得10
22秒前
科研通AI2S应助科研通管家采纳,获得10
23秒前
赘婿应助科研通管家采纳,获得10
23秒前
Ava应助科研通管家采纳,获得10
23秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989444
求助须知:如何正确求助?哪些是违规求助? 3531531
关于积分的说明 11254250
捐赠科研通 3270191
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174