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

自然语言处理中的一个关键挑战是定义单词的计算表示。 数据驱动的分布式方法使用语料库来诱导词的向量空间表示，基于它们出现的上下文。 该项目超越了传统的方法（例如，潜在语义分析; Deerwester等人，1990），其通过扩展用于构建语义向量的上下文类型，使用倾向于出现在语料库中的词附近的词来定义上下文。 首先，该项目纳入了翻译语境，即，与传统的单语语料库一起，在多语言平行语料库中随时可用的单词。 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资源贫乏的语言。 其次，该项目结合了从社交网络平台推断的社交背景，通过作者，时间，地理和社交连接元数据捕获。 总之，这些额外的功能给出了一个词的上下文更广泛的定义，并导致一个更统一的方法来建模人类语言的分布式方法，朝着语言独立的语义方向发展。 该项目侧重于代表几个主要语系的十种类型多样的语言（英语，阿拉伯语，汉语，西班牙语，俄语，德语，葡萄牙语，斯瓦希里语，马达加斯加语和波斯语）。一个关键的重点是扩大算法，用于推断网络规模的语料库的分布表示和处理更大的上下文向量表示扩展的上下文概念。 该方法还利用嘈杂的句法处理，使句法信息，而不仅仅是信息相邻的话，被认为是在定义context.In除了提高学习的词汇语义表示的质量，包括更丰富的上下文信息，该项目创建词汇语义表示，跨语言链接的单词类型。这些都直接用于文本处理应用程序，如文本分类，机器翻译，信息提取和文本的语义分析，它们将使强大的词汇语义资源在低资源的语言，受益于丰富的资源，他们配对的语言建设。 所产生的多语言矢量表示将发布给研究界，并将用于本科生课程项目。 该项目为两名研究生在动态研究环境中提供综合教育和研究经验。 项目网站（http：//www.ark.cs.cmu.edu/BigMultilinguality）将用于传播成果。

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.