BIGDATA: Small: DA: Big Multilinguality for Data-Driven Lexical Semantics

BIGDATA：小：DA：数据驱动词汇语义的大多语言性

• 批准号: 1251131
• 负责人: Noah Smith
• 金额: $ 25万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251131&HistoricalAwards=false
• 关键词: BIGDATA; Small; DA; Big; Multilinguality

A key challenge in natural language processing is defining the computational representation of words. Data-driven distributional approaches use corpora to induce vector-space representations for words, based on the contexts they occur in. This project goes beyond traditional approaches (e.g., latent semantic analysis; Deerwester et al., 1990), which use words that tend to occur near a word in corpora to define the context, by extending the types of contexts used in constructing semantic vectors. First, this project incorporates translation contexts, i.e., words readily available in multilingual parallel corpora, alongside traditional monolingual corpora. This allows evidence-sharing across languages, most importantly from resource-rich languages with large corpora to more resource-poor languages. Second, this project incorporates social context inferable from social network platforms, captured through author, time, geographic, and social connection metadata. Taken together, these additional features give a broader definition of a word's context and lead to a more unified approach to the distributional approach to modeling human language, moving in the direction of a language-independent semantics. The project focuses on ten typologically diverse languages representing several major language families (English, Arabic, Chinese, Spanish, Russian, German, Portuguese, Swahili, Malagasy, and Farsi). A key emphasis is scaling up algorithms for inferring distributional representations to web-scale corpora and dealing with much larger contextual vectors representing the expanded notion of context. The approach also leverages noisy syntactic processing to enable syntactic information, rather than just information about neighboring words, to be considered when defining context.In addition to improving the quality of the learned lexico-semantic representations by including richer contextual information, this project creates lexical semantic representations that link word types across languages. These have direct use in text processing applications such as text categorization, machine translation, information extraction, and semantic analysis of text, and they will enable the construction of robust lexical semantic resources in lower-resource languages that benefit from the richness of resources in languages they are paired with. The multilingual vector representations produced will be released to the research community and will be used in undergraduate class projects. The project provides integrated educational and research experience for two graduate students in a dynamic research environment. The project website (http://www.ark.cs.cmu.edu/BigMultilinguality) will be used for dissemination of results.

自然语言处理中的一个关键挑战是定义单词的计算表示。 基于它们发生的上下文，数据驱动的分布方法使用CORPORA来诱导单词的矢量空间表示。该项目超越了传统方法（例如，潜在的语义分析； Deerwester等，1990），这些单词倾向于在Corpora附近发生的单词来定义上下文，通过扩展上下文，以扩展上下文，以扩展语境构建语言vectors semantic vectors。 首先，该项目结合了翻译上下文，即在多语言平行语料库中随时可用的单词以及传统的单语言语料库。 这允许跨语言共享证据，最重要的是，从拥有大型语料库的资源丰富的语言到更多资源贫乏的语言。 其次，该项目结合了可从社交网络平台中推断出的社会环境，这些平台通过作者，时间，地理和社会联系元数据捕获。 综上所述，这些附加功能给出了对单词上下文的更广泛的定义，并为建模人类语言的分配方法提供了更统一的方法，并朝着独立于语言的语义的方向发展。 该项目着重于代表几种主要语言家庭的十种类型上多样化的语言（英语，中文，西班牙语，俄语，德语，葡萄牙语，斯瓦希里语，马拉加尼和法尔西）。一个关键的重点是扩展算法将分布表示为网络规模的语料库，并处理代表上下文概念的更大的上下文向量。 该方法还利用嘈杂的句法处理来启用句法信息，而不是仅仅在定义上下文时考虑有关相邻单词的信息。除了通过包括更丰富的上下文信息包括较丰富的上下文信息来提高学习词典的语义语义表示的质量，该项目创建该项目创建链接跨语言类型的词汇语义表示。这些直接用于文本处理应用程序，例如文本分类，机器翻译，信息提取和文本的语义分析，它们将在低资源语言中构建强大的词汇语义资源，这些语言受益于与它们与之配对的语言的丰富性。 产生的多语​​言矢量表示将发布给研究社区，并将用于本科课程项目。 该项目在动态研究环境中为两位研究生提供了综合的教育和研究经验。 项目网站（http://www.ark.cs.cmu.edu/bigmultlinguality）将用于传播结果。