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.

今天的知识工作者的工作涉及处理或使用信息，他们的主要限制包括(a)他们必须阅读和消化的文本数量，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数量。在专门语料库上进行文本密集型任务的例子包括：为编写系统评论而对给定主题进行文献检索；在客户服务或在线社区中检索专利、法院判决或事件报告的高召回率；搜索和浏览电子医疗记录或与健康相关的列表服务器内容，以获取嵌入在免费文本中的隐性知识；对有研究课题的论文进行批注。社交媒体等非正式文本的例子包括谣言检测和传播，动态话题检测和跟踪，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：