Semantic Methods for Computer-supported Writing Aids

这一语言技术领域的研究建议关注的问题是，是否有可能开发一种语义写作辅助工具，它有助于通过释义来重新表述文本。这类似于拼写校正或语法校正：在文本处理上下文中，提供合适的释义，这允许更快地用更多可变的词汇选择来表达文本。一个特殊的功能是通过研究一种机制，提高释义质量（并以这种方式的写作援助）的使用数据。该提案的主要假设是，我们假设无监督和无知识的方法可以为该应用程序上下文的释义产生合适的数据源。一个释义组件被发现在这个写作援助的原型实现的核心。除了关于多个数据源的组合和关于合适的用户界面的研究的问题之外，我们还将使用数据驱动的方法将方法转移到其他语言。此外，我们还探讨了利用内隐反馈改进写作辅助工具的可能性。对于单个组件的开发以及模拟使用，我们大量依赖众包作为数据收集和评估的手段。首先，我们探讨，如何从不同的数据源的释义的特点和联合收割机这些异构的源在一个释义组件。我们语境化的分布式语义方法产生上下文相关的释义候选人与无监督和知识的方法。在这里，我们特别关注这种方法的数据驱动性，理想情况下，它应该在没有任何现有词汇资源的情况下工作。这是出于语言和领域的独立性，并将通过将方法从英语转移到德语来证明。在用户研究的支持下，我们实现了一个写作辅助工具的原型，并在用户交互、演示和主动性方面优化了解决方案。我们使用这个原型来检查，在多大程度上，我们可以使用“弱信号”，即仅仅是交互数据与原型，以改善释义组件。这种形式的隐式用户反馈，还没有被利用在语言技术之前，引起了语言处理组件的迭代细化，以segue预处理步骤的应用程序所需的质量水平。

This research proposal in the field of language technology is concerned with the question whether it is possible to develop a semantic writing aid, which helps reformulating texts by paraphrasing. This works similar to a spelling correction or grammar correction: in a text processing context, suitable paraphrases are offered, which allows faster formulation of texts with a more variable vocabulary choice. A special feature is given by researching a mechanism that improves paraphrasing quality (and in this way the writing aid) by usage data. The main hypothesis in this proposal is that we assume that unsupervised and knowledge-free methods can yield suitable data sources for paraphrases for this application context. A paraphrasing component is found at the core of a prototypical implementation of this writing aid. Additionally to questions regarding the combination of several data sources and research regarding a suitable user interface, we will work on transferring the methodology to other languages using data-driven methods. Further, we explore the possibility of improving the writing aid with implicit feedback. For the development of single components, as well as for simulating usage, we massively rely on crowdsourcing as a means to data collection and for evaluation. First we explore, how paraphrases from different data sources are characterized and combine these heterogeneous sources in a paraphrasing component. We contextualize distributional semantic methods to produce context-dependent paraphrase candidates wit unsupervised and knowledge-free methods. Here, we especially focus on the data-drivenness of this approach, which should ideally work without any existing lexical resources. This is motivated by language and domain independence and will be demonstrated by transferring the methodology from English to German. Supported by user studies, we implement a prototype of the writing aid and optimize the solution regarding user interaction, presentation and pro-activity. We use this prototype to examine, in how far we can use 'weak signals', i.e. merely interaction data with the prototype, to improve the paraphrasing component. This form of implicit user feedback, that has not been utilized in language technology before, gives rise to the iterative refinement of language processing components in order to segue pre-processing steps to the quality level required by applications.