Semantic Representations for Interactive Text Mining

当今知识工作者的工作涉及处理或使用信息，其主要局限性包括：（a）他们必须阅读和消化的文本量，以及（B）他们花在搜索、收集和组织文本形式的信息上的时间量。专门语料库上的文本密集型任务的例子包括：为编写系统综述而对给定主题进行文献检索;在客户服务或在线社区中对专利、法院判决或事件报告进行高召回率检索;搜索和浏览电子病历或与健康相关的列表服务器内容，以寻找自由文本中嵌入的隐性知识;以及对论文进行研究主题注释。社交媒体等非正式文本的例子包括谣言检测和传播，动态主题检测和跟踪，以及社会学研究中的访谈分析。这些用例背后的核心研究问题包括：（1）文档的语义检索，解决相关文档之间的词汇不匹配问题;（2）半结构化知识库（如维基百科）以及弱组织的特定领域语料库的利用;（3）处理文本数据的动态特性，包括概念漂移，并灵活处理较短或较长的时间框架;（4）需要人在回路中的文本挖掘，以引导算法为个人用户产生相关结果。这需要交互式可视化和算法开放给用户交互。 语义相关性方法是基于单词和文档嵌入提出的，这些嵌入是从各种深度网络架构的无监督训练中获得的，用于大型文本语料库中的单词或句子预测等任务。这样的嵌入已经证明了在许多有监督的下游自然语言处理任务上的先进性。然而，基于嵌入的语义文本表示，这是密集的数字向量，和人类的直觉，其启发需要交互式的视觉界面，有效地涉及非技术用户之间存在差距。拟议的研究将旨在通过专注于可解释的机器学习算法和表示来填补这一空白，而不是黑箱。更进一步，我们将建立在交互性的基础上，以实现可解释性，使人类能够有效地引导机器学习获得有意义的结果。 总的来说，我们的目标是下一代视觉文本分析系统，该系统基于基于深度网络的现代单词，术语和文档嵌入的功能，以捕获比词袋表示更好的语义，而不会失去基于单词和术语的可视化的直观性。拟议的研究将有助于可解释深度网络的新兴研究领域，专门用于支持知识工作者的交互式机器学习。

Key limitations of today's knowledge workers, whose job involves handling or using information, include (a) the amount of text they have to read and digest, and (b) the amount of time they spend searching for, gathering and organizing information in text form. Examples of text-intensive tasks on specialized corpora include: literature search on a given topic for compilation of a systematic review; high-recall retrieval of patents, court decisions or incident reports in customer service or online communities; search and browsing of electronic medical records or health-related listserver content for tacit knowledge embedded in free text; and annotation of papers with research topics. Examples of informal text such as social media include rumour detection and propagation, dynamic topic detection and tracking, and analysis of interviews in sociology research. Core research problems underlying these use cases include: (1) Semantic retrieval of documents, addressing vocabulary mismatch across related documents; (2) The exploitation of semi-structured knowledge bases, such as Wikipedia, as well as weakly organized domain-specific corpora; (3) Handling the dynamic nature of the text data, including concept drift, and flexibly handling shorter or longer time frames; (4) The need for the human-in-the-loop text mining, to guide the algorithms towards producing relevant results for the individual user. This requires interactive visualizations and algorithms open to user interaction. Semantic relatedness methods have been proposed based on word and document embeddings derived from unsupervised training of various deep network architectures on tasks such as word or sentence prediction in large text corpora. Such embeddings have demonstrated advances to the state of the art on a number of supervised downstream natural language processing tasks. However, a gap exists between semantic text representations based on embeddings, which are dense numeric vectors, and human intuition, whose elicitation requires interactive visual interfaces to involve a non-technical user effectively. The proposed research will aim to fill this gap by focusing on explainable, as opposed to black box, machine learning algorithms and representations. Taking this one step further, we will build on interactivity to achieve explainability, allowing the human to efficiently steer the machine learning towards meaningful results. Overall, we will aim for the next-generation visual text analytics systems that build on the capabilities of modern word, term and document embeddings based on deep networks to capture semantics better than the bag-of-words representations, without losing the intuitive nature of word- and term-based visualizations. The proposed research will be a contribution to the emerging research area of explainable deep networks, specialized to interactive machine learning for supporting knowledge workers.