论文标题
使用NLP可行的短语检测
Actionable Phrase Detection using NLP
论文作者
论文摘要
可行的句子是最基本的术语，意味着采取特定行动的必要性。用语言来说，它们通常是通过动作动词的使用来实现操作的步骤。例如，句子“让您的作业明天完成”具有可行的条件，因为它要求采取特定的措施（在这种情况下，完成作业）。相比之下，一个简单的句子，例如，“我喜欢弹吉他”并不是一个可操作的短语，因为它只是说明了个人的个人选择，而不是要求完成任务。 在本文中，目的是探索是否可以使用从头开始设计的语言过滤器从原始文本中提取ActionAbles。这些过滤器专门针对使用转移学习作为主要角色的可行文本。可操作的检测可用于检测危机期间的紧急任务，急救的指导准确性，还可以用于制造诸如会议中的自动待办事项列表生成器之类的生产力工具。为此，我们使用Enron电子邮件数据集，并将我们的语言过滤器应用于清洁的文本数据。然后，我们将转移学习与通用句子编码器一起训练模型，以分类给定的原始文本是否可以起作用。
Actionable sentences are terms that, in the most basic sense, imply the necessity of taking a specific action. In Linguistic terms, they are steps to achieve an operation, often through the usage of action verbs. For example, the sentence, `Get your homework finished by tomorrow` qualifies as actionable since it demands a specific action (In this case, finishing homework) to be taken. In contrast, a simple sentence such as, `I like to play the guitar` does not qualify as an actionable phrase since it simply states a personal choice of the person instead of demanding a task to be finished. In this paper, the aim is to explore if Actionables can be extracted from raw text using Linguistic filters designed from scratch. These filters are specially catered to identifying actionable text using Transfer Learning as the lead role. Actionable Detection can be used in detecting emergency tasks during a crisis, Instruction accuracy for First aid and can also be used to make productivity tools like automatic ToDo list generators from conferences. To accomplish this, we use the Enron Email Dataset and apply our Linguistic filters on the cleaned textual data. We then use Transfer Learning with the Universal Sentence Encoder to train a model to classify whether a given string of raw text is actionable or not.
