Advancing the Performance of Word Sense Disambiguation by Finding Consistent Criteria for Sense Distinctions
通过寻找语义区分的一致标准来提高词义消歧的性能
基本信息
- 批准号:0415923
- 负责人:
- 金额:$ 50万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-12-15 至 2007-02-28
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The goal of this project is to provide automatic word sense disambiguation systems based on a principled English sense inventory geared to information processing needs. Polysemy, the many possible interpretations, or senses, of a word, is one of the major bottlenecks to accurate and focused information processing. Past attempts to use a public domain resource, WordNet, as a sense inventory for creating training data have not been successful due to vague and subtle sense distinctions that lead to poor inter-annotator agreement. We are experimenting with approaches to group fine-grained WordNet senses into more coarse-grained sense distinctions that can be annotated more rapidly and more accurately. Using linguistic evidence, we are refining our methodology for grouping word senses and our annotation process while creating large amounts of sense-tagged text. This new sense inventory has links to WordNet, FrameNet, and VerbNet, with clear criteria associated with the sense distinctions that facilitate accurate human sense tagging. Using the annotated data we are developing accurate supervised and semi-supervised automatic word sense disambiguation systems by experimenting with different machine learning algorithms and feature sets. The sense inventory, the tagged data, and the trained systems will all be in the public domain for both national and international access, providing a stable English sense inventory geared to computational applications. The availability of broad coverage automatic word sense disambiguation systems will provide a major boost in performance to information retrieval, information extraction, question answering and machine translation, improving our ability to stay abreast of the information avalanche.
该项目的目标是提供基于面向信息处理需求的原则性英语义库的自动词义消歧系统。一词多义,即一个词的多种可能的解释或意义,是准确和集中信息处理的主要瓶颈之一。过去使用公共领域资源WordNet作为用于创建训练数据的词义清单的尝试没有成功,这是因为模糊和微妙的词义区别导致较差的注释者之间的一致性。我们正在试验将细粒度的WordNet词义分组为更粗粒度的词义区别的方法,以便更快、更准确地进行注释。使用语言学证据,我们正在改进我们对词义进行分组的方法和我们的注释过程,同时创建大量带有意义标记的文本。这个新的感觉清单链接到WordNet、FrameNet和VerbNet,具有与有助于准确的人类感觉标记的意义区分相关的明确标准。利用标注后的数据,通过实验不同的机器学习算法和特征集,我们正在开发准确的监督和半监督的自动词义消歧系统。SENSE清单、标记数据和训练的系统都将在公共领域内供国内和国际访问,提供面向计算应用的稳定的英语SENSE清单。广泛覆盖的自动词义消歧系统的可用将大大提高信息检索、信息提取、问答和机器翻译的性能,提高我们跟上信息雪崩的能力。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Martha Palmer其他文献
A Case for Rule-Driven Semantic Processing
规则驱动的语义处理案例
- DOI:
10.3115/981923.981958 - 发表时间:
1981 - 期刊:
- 影响因子:0
- 作者:
Martha Palmer - 通讯作者:
Martha Palmer
A Large-Scale Extension of VerbNet with Novel Verb Classes
VerbNet 的大规模扩展与新颖的动词类
- DOI:
- 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
K. Kipper;A. Korhonen;Neville Ryant;Martha Palmer - 通讯作者:
Martha Palmer
VerbNet Representations: Subevent Semantics for Transfer Verbs
VerbNet 表示:转移动词的子事件语义
- DOI:
10.18653/v1/w19-3318 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
S. Brown;Julia Bonn;James Gung;A. Zaenen;J. Pustejovsky;Martha Palmer - 通讯作者:
Martha Palmer
Good Seed Makes a Good Crop: Accelerating Active Learning Using Language Modeling
好种子结出好庄稼:使用语言建模加速主动学习
- DOI:
- 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Dmitriy Dligach;Martha Palmer - 通讯作者:
Martha Palmer
SCI 3.0: A Web-based Schema Curation Interface for Graphical Event Representations
SCI 3.0:用于图形事件表示的基于 Web 的模式管理界面
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Reece Suchocki;Mary Martin;Martha Palmer;S. Brown - 通讯作者:
S. Brown
Martha Palmer的其他文献
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{{ truncateString('Martha Palmer', 18)}}的其他基金
RI:Medium:Collaborative Research: Developing a Uniform Meaning Representation for Natural Language Processing
RI:中:协作研究:为自然语言处理开发统一的含义表示
- 批准号:
1764048 - 财政年份:2018
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
CI-P: Collaborative Research: LexLink: Aligning WordNet, FrameNet, PropBank and VerbNet
CI-P:协作研究:LexLink:对齐 WordNet、FrameNet、PropBank 和 VerbNet
- 批准号:
1205484 - 财政年份:2012
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
RI: Small: A Bayesian Approach to Dynamic Lexical Resources for Flexible Language Processing
RI:小:用于灵活语言处理的动态词汇资源的贝叶斯方法
- 批准号:
1116782 - 财政年份:2011
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
RI: Large: Collaborative Research: Richer Representations for Machine Translation
RI:大型:协作研究:更丰富的机器翻译表示
- 批准号:
0910992 - 财政年份:2009
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
Collaborative Research: CRI: CRD: A Multi-Representational and Multi-Layered Treebank for Hindi/Urdu
合作研究:CRI:CRD:印地语/乌尔都语的多表征和多层树库
- 批准号:
0751202 - 财政年份:2008
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
CRI:CRD Collaborative Research: General Techniques for Creating Treebanks with Multiple Representations: A Large-Scale Russian Application
CRI:CRD 协作研究:创建具有多种表示的树库的通用技术:俄罗斯的大规模应用
- 批准号:
0709167 - 财政年份:2007
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Advancing the Performance of Word Sense Disambiguation by Finding Consistent Criteria for Sense Distinctions
通过寻找语义区分的一致标准来提高词义消歧的性能
- 批准号:
0715078 - 财政年份:2006
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
MLIAM: ISLE-International Standards for Language Engineering
MLIAM:ISLE-语言工程国际标准
- 批准号:
9910603 - 财政年份:2000
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
Associating Semantic features with Intersective Levin classes
将语义特征与 Intersective Levin 类关联
- 批准号:
9800658 - 财政年份:1998
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
Experimenting with Different Control Structures for Text Analysis
尝试不同的控制结构进行文本分析
- 批准号:
9412898 - 财政年份:1995
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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