The Calculation of Verb Similarity Based on Ternary Morpheme Collocation

自然语言处理 动词 计算机科学 搭配(遥感) 人工智能 语义相似性 语言学 相似性(几何) 语素 语法 对象(语法) 语义学(计算机科学) 词汇 数学
作者
Tian Shao,Endong Xun,Guirong Wang,Chengwen Wang,Gaoqi Rao,Bo Xia
标识
DOI:10.1109/ialp54817.2021.9675253
摘要

Currently, there are about three issues in calculating the similarity of Chinese vocabulary: one is that the calculation of vocabulary similarity generally only focuses on the semantic similarity of words, and the grammatical similarity of words is not paid enough attention; the second is that there is no relevant research to calculate similarity for a part of speech; third, the collocation relationship between words is not fully used. By targeting these three questions, this article locates the verb research object and uses the collocation information between words to calculate the grammatical and semantic similarity of the verb. Firstly, based on the dictionary of verb synonyms, using dependent data to construct the ternary collocation relationship between the verb and its subject-object core morphemes, and embed the ternary collocation information into the vector representation of the verb, finally using the cosine formula to calculate the grammatical and semantic similarity of the verb. According to the different results, the synonyms of a verb are divided into synonyms with the same grammar and semantics, synonyms with similar grammar and semantics, and synonyms with similar semantics. By tagging these three types of labels on the synonyms of verbs, more grammatical and semantic information is provided for clause-level retelling.
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
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
mimi发布了新的文献求助10
刚刚
呆呆完成签到,获得积分10
1秒前
blebui应助姜茶采纳,获得10
1秒前
幼稚园小新完成签到,获得积分10
1秒前
123完成签到,获得积分10
1秒前
2秒前
snowball完成签到,获得积分10
2秒前
3秒前
duoduozs发布了新的文献求助10
3秒前
velpro完成签到,获得积分10
3秒前
qqqq完成签到,获得积分10
3秒前
4秒前
4秒前
溪风完成签到,获得积分10
4秒前
ting发布了新的文献求助10
5秒前
6秒前
Xxxnnian发布了新的文献求助30
6秒前
听风暖完成签到 ,获得积分10
7秒前
li发布了新的文献求助10
7秒前
赘婿应助伊布采纳,获得10
7秒前
gaga完成签到,获得积分10
7秒前
小蘑菇应助reck采纳,获得10
8秒前
清风荷影完成签到 ,获得积分10
8秒前
酷波er应助动如脱兔采纳,获得10
9秒前
9秒前
9秒前
9秒前
10秒前
圈圈发布了新的文献求助10
10秒前
易达发布了新的文献求助10
10秒前
追梦人完成签到,获得积分10
10秒前
10秒前
实验室扛把子完成签到,获得积分10
10秒前
在水一方应助清爽忆山采纳,获得10
11秒前
小马甲应助日月山河永在采纳,获得10
11秒前
娃娃发布了新的文献求助10
12秒前
12秒前
任医生发布了新的文献求助10
12秒前
冷眼观潮完成签到,获得积分10
12秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527304
求助须知:如何正确求助?哪些是违规求助? 3107454
关于积分的说明 9285518
捐赠科研通 2805269
什么是DOI,文献DOI怎么找? 1539827
邀请新用户注册赠送积分活动 716708
科研通“疑难数据库(出版商)”最低求助积分说明 709672