Software Open Access
Many researchers working with historical newspapers have the problem that their data sets or automated set annotations contain articles that are irrelevant to their research question. For example, if the goal is to find articles on return migration, researchers have to deal with some ambiguous search terms. The German words "Heimkehr" (returning home) or "Rückkehr" (returning back) lead to many articles that are relevant to the research question, but also to articles that are not relevant (e.g. return from a mountain tour, work, etc.). By using topic models and document similarity measurements, this notebook allows me to exclude these articles without combining the word "Heimkehr" with other search terms. Furthermore, the same code can also be used to remove or prefer a certain genre, e.g. advertising, sports news, etc.
To give another example: If I want to create a collection of articles about the disease cancer, one of the important German words for cancer is "Krebs". But "Krebs" in German is also a common surname, an animal (crab) or a sign of the zodiac.
The main purpose of this notebook is to take into account the context of articles in order to automatically refine a search query. This means that even ambiguous words can be used for the search without having to combine them with other words, making the search less influenced by the researcher's prior knowledge and avoiding a too narrow tunnel vision.