In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; iii. and event analytics.
The work by Yeung and Jatowt @cite_7 tackles the problem of analysis of historical events in multiple large document collections. They utilize to identify distributions along time. Thereafter they perform analytics to answer questions such as significant years and topics, triggers that caused remembrance of the past and historical similarity of countries.
@cite_7
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History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societ al views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support.