diff --git a/README.md b/README.md
index 76823d2..9ef706b 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,7 @@ To visualize the incidents on a map, the district on its own seemed to be an imp
 Doing this we realized that some of the words we extracted from the text were actually completely irrelevant (*hair*, *face*, *woman*, *evening* and the like). We supposed to be able to identify these irrelevant ones by simply querying a german dictionary whether it contains this word or not, to tell whether this word is a relevant location or just an irrelevant noun from the german language. We ended up checking a text file, containing around 24,000 german nouns.
 
 ## Categorizing incidents
-As we examined some of the incident descriptions we came to the conclusion that it is possible to group most of the incidents to certain categories. We recognized four major categories: *homophic* incidents, *antisemtitic* incidents, *sexist* incidents and *racist* incidents. To automatically assign distinctive categories to each incident we implemented a simple algorithm which searches for certain keywords in the incident description. This way an incidents can be tagged with none or multiple categories ([analyze.py](analyze.py)). We stored these assignements in a seperate table of our SQLite database called *category* with the columns *ID*, *Name* and *Article_ID*.
+As we examined some of the incident descriptions we came to the conclusion that it is possible to group most of the incidents by certain categories. We recognized four major categories: *homophic* incidents, *antisemtitic* incidents, *sexist* incidents and *racist* incidents. To automatically assign distinctive categories to each incident we implemented a simple algorithm which searches for certain keywords in the incident description. This way an incidents can be tagged with none or multiple categories ([analyze.py](analyze.py)). We stored these assignements in a seperate table of our SQLite database called *category* with the columns *ID*, *Name* and *Article_ID*.
 
 ```
 bad_words = {