文本语料库中预定义主题动态显著性量化的NLP方法
An NLP approach to quantify dynamic salience of predefined topics in a text corpus
摘要：
同时在线新闻媒体的激增，为分析人员提供了宝贵的资源，也对他们提出了重大挑战，这些分析人员的目的是描绘和了解感兴趣的地理位置的社会和文化趋势。虽然大量记录重大事件、趋势和反应的新闻报道提供了一个更加民主化的地点社会特征的图景，但对任何一个分析师或团队来说，理解整个语料库以提取重大趋势都是一个巨大的挑战。在这里，我们提出了一种方法，使用自然语言处理技术，旨在量化一组预定义的感兴趣的主题如何随着时间的推移在一个大的文本语料库的变化。我们发现，给定一个预定义的主题，我们可以识别和排名的术语集，或n-gram，映射到这些主题，并具有偏离正常基线的使用模式。出现，消失，或显着的变化，在n-gram的使用提出了一个地面上的图片，一个主题的动态显着性在一个语料库的兴趣。
英文摘要：
The proliferation of news media available online simultaneously presents a valuable resource and significant challenge to analysts aiming to profile and understand social and cultural trends in a geographic location of interest. While an abundance of news reports documenting significant events, trends, and responses provides a more democratized picture of the social characteristics of a location, making sense of an entire corpus to extract significant trends is a steep challenge for any one analyst or team. Here, we present an approach using natural language processing techniques that seeks to quantify how a set of pre-defined topics of interest change over time across a large corpus of text. We found that, given a predefined topic, we can identify and rank sets of terms, or n-grams, that map to those topics and have usage patterns that deviate from a normal baseline. Emergence, disappearance, or significant variations in n-gram usage present a ground-up picture of a topic's dynamic salience within a corpus of interest.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
