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
刚刚
王小西完成签到,获得积分10
1秒前
zzx完成签到,获得积分10
1秒前
量子星尘发布了新的文献求助50
4秒前
学术乞丐发布了新的文献求助10
7秒前
万能图书馆应助怡然问晴采纳,获得10
7秒前
Akim应助体贴凌柏采纳,获得10
8秒前
zyq完成签到,获得积分10
9秒前
西瓜完成签到,获得积分10
10秒前
xshzhou完成签到,获得积分10
12秒前
苗条白枫完成签到 ,获得积分10
13秒前
一棵草完成签到,获得积分10
13秒前
内向的跳跳糖完成签到,获得积分10
13秒前
遇见飞儿完成签到,获得积分0
13秒前
cream完成签到,获得积分20
14秒前
14秒前
小薛完成签到,获得积分10
14秒前
15秒前
Cu_wx完成签到,获得积分10
15秒前
噜噜噜噜噜完成签到,获得积分10
17秒前
赵慧霞关注了科研通微信公众号
17秒前
炎魔之王拉格纳罗斯完成签到,获得积分10
18秒前
内向苡完成签到,获得积分10
19秒前
以筱发布了新的文献求助10
21秒前
bhkwxdxy完成签到,获得积分10
22秒前
悦耳虔纹完成签到 ,获得积分10
22秒前
xx完成签到,获得积分10
22秒前
大气灵枫完成签到,获得积分10
22秒前
妮妮完成签到,获得积分10
23秒前
25秒前
Struggle完成签到 ,获得积分10
26秒前
26秒前
秦兴虎完成签到,获得积分10
27秒前
Drew11完成签到,获得积分10
27秒前
风趣青槐完成签到,获得积分10
29秒前
科隆龙完成签到,获得积分10
30秒前
30秒前
饱满一手完成签到 ,获得积分10
30秒前
99完成签到,获得积分10
32秒前
枕星发布了新的文献求助10
32秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038303
求助须知:如何正确求助?哪些是违规求助? 3576013
关于积分的说明 11374210
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029