Semantic Representations for Interactive Text Mining

交互式文本挖掘的语义表示

• 批准号: RGPIN-2020-04834
• 负责人: Milios, Evangelos
• 金额: $ 2.55万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717181
• 关键词: Semantic; Representations; Interactive; Text; Mining

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.

当今知识工作者的关键局限性涉及处理或使用信息，包括（a）他们必须阅读和消化的文本量，以及（b）他们花费的时间来搜索，以文本形式收集和组织信息。有关专业语料库的文本密集型任务的示例包括：有关给定主题的文献搜索，以汇编系统审查；在客户服务或在线社区中的专利，法院裁决或事件报告的高召回检索；搜索和浏览电子病历或与健康相关的列表服务器内容，以嵌入自由文本中的隐性知识；和研究主题的论文注释。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访谈的分析。 这些用例的核心研究问题包括： （1）文档的语义检索，解决相关文档之间词汇不匹配的问题； （2）剥削半结构化的知识库，例如Wikipedia，以及弱组织的特定领域特定的语料库； （3）处理文本数据的动态性质，包括概念漂移，并灵活处理较短或更长的时间范围； （4）需要人类的文本挖掘，以指导算法为单个用户产生相关结果。这需要交互式可视化和算法向用户交互开放。 语义相关性方法是基于单词和文档嵌入而提出的，这些嵌入是根据对大型文本语料库中单词或句子预测等任务的各种深层网络体系结构进行的无监督培训得出的。这种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对最新技术的进步。但是，基于嵌入的语义文本表示之间存在差距，这些语义文本表示是密集的数字向量和人类直觉，其启发需要交互式视觉接口才能有效涉及非技术用户。拟议的研究旨在通过专注于可解释的，而不是黑匣子，机器学习算法和表示来填补这一空白。进一步迈出一步，我们将以交互性为基础，以实现解释性，使人有效地将机器学习迈向有意义的结果。 总体而言，我们将建立下一代视觉文本分析系统，这些系统基于基于深层网络的现代单词，术语和文档嵌入的功能，以比单词袋表示更好地捕获语义，而不会丢失单词和基于术语的可视化的直觉性质。拟议的研究将是对可解释的深网的新兴研究领域的贡献，该研究专门用于交互式机器学习，以支持知识工作者。