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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750276
• 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）他们花费在搜索、收集和组织文本形式的信息上的时间。专门语料库上的文本密集型任务的例子包括：针对给定主题的文献检索以编制系统综述；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，以获取嵌入自由文本的隐性知识；以及研究主题论文的注释。社交媒体等非正式文本的示例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）利用半结构化知识库，例如维基百科，以及弱组织的特定领域语料库； （3）处理文本数据的动态特性，包括概念漂移，灵活处理更短或更长的时间范围； (4) 需要进行人机交互文本挖掘，以指导算法为单个用户生成相关结果。这需要对用户交互开放的交互式可视化和算法。 人们提出了基于单词和文档嵌入的语义相关性方法，这些嵌入来自对大型文本语料库中的单词或句子预测等任务的各种深度网络架构的无监督训练。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的最新技术进步。然而，基于嵌入的语义文本表示（密集的数字向量）与人类直觉之间存在差距，人类直觉的引发需要交互式视觉界面来有效地吸引非技术用户。拟议的研究旨在通过关注可解释的机器学习算法和表示（而不是黑盒）来填补这一空白。更进一步，我们将在交互性的基础上实现可解释性，让人类能够有效地引导机器学习取得有意义的结果。 总体而言，我们的目标是下一代视觉文本分析系统，该系统建立在基于深度网络的现代单词、术语和文档嵌入的功能之上，能够比词袋表示更好地捕获语义，同时又不失基于单词和术语的可视化的直观本质。拟议的研究将为可解释的深度网络这一新兴研究领域做出贡献，专门用于支持知识工作者的交互式机器学习。