Linguistic complexity: locality of syntactic dependencies

地点 句子处理 判决 计算机科学 对象(语法) 自然语言处理 解析 航程(航空) 模棱两可 组分(热力学) 人工智能 语言学 认知心理学 心理学 哲学 复合材料 材料科学 程序设计语言 物理 热力学
作者
Edward Gibson
出处
期刊:Cognition [Elsevier]
卷期号:68 (1): 1-76 被引量:1871
标识
DOI:10.1016/s0010-0277(98)00034-1
摘要

This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory – the Syntactic Prediction Locality Theory (SPLT) – has two components: an integration cost component and a component for the memory cost associated with keeping track of obligatory syntactic requirements. Memory cost is hypothesized to be quantified in terms of the number of syntactic categories that are necessary to complete the current input string as a grammatical sentence. Furthermore, in accordance with results from the working memory literature both memory cost and integration cost are hypothesized to be heavily influenced by locality (1) the longer a predicted category must be kept in memory before the prediction is satisfied, the greater is the cost for maintaining that prediction; and (2) the greater the distance between an incoming word and the most local head or dependent to which it attaches, the greater the integration cost. The SPLT is shown to explain a wide range of processing complexity phenomena not previously accounted for under a single theory, including (1) the lower complexity of subject-extracted relative clauses compared to object-extracted relative clauses, (2) numerous processing overload effects across languages, including the unacceptability of multiply center-embedded structures, (3) the lower complexity of cross-serial dependencies relative to center-embedded dependencies, (4) heaviness effects, such that sentences are easier to understand when larger phrases are placed later and (5) numerous ambiguity effects, such as those which have been argued to be evidence for the Active Filler Hypothesis.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
哈尼酱完成签到 ,获得积分10
1秒前
1秒前
2秒前
homeworkk发布了新的文献求助10
2秒前
潦草发布了新的文献求助10
3秒前
4秒前
Hippocampus发布了新的文献求助10
4秒前
5秒前
zzy发布了新的文献求助10
5秒前
5秒前
hoojack发布了新的文献求助10
6秒前
7秒前
8秒前
10秒前
qzxwsa发布了新的文献求助10
11秒前
11秒前
刘琪琪发布了新的文献求助10
11秒前
11秒前
12秒前
12秒前
12秒前
FashionBoy应助一路硕博采纳,获得10
12秒前
你想读博吗应助研友_Lw4vGn采纳,获得10
12秒前
Doris发布了新的文献求助10
13秒前
Hippocampus完成签到,获得积分10
13秒前
zzy完成签到,获得积分10
13秒前
芒果干完成签到,获得积分10
13秒前
13秒前
烟花应助Aegean采纳,获得10
14秒前
14秒前
hoojack发布了新的文献求助10
15秒前
李健的粉丝团团长应助lwq采纳,获得10
16秒前
lyn完成签到,获得积分10
16秒前
CLH发布了新的文献求助50
17秒前
酷炫半青发布了新的文献求助10
17秒前
18秒前
18秒前
理想三寻发布了新的文献求助10
18秒前
小羊发布了新的文献求助10
18秒前
19秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Foreign Policy of the French Second Empire: A Bibliography 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
Classics in Total Synthesis IV 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3145621
求助须知:如何正确求助?哪些是违规求助? 2797097
关于积分的说明 7822848
捐赠科研通 2453435
什么是DOI,文献DOI怎么找? 1305652
科研通“疑难数据库(出版商)”最低求助积分说明 627514
版权声明 601469