Publications/Topic_IJHCC.md

@@ -10,5 +10,17 @@ The approach is two-tiered. First, we disambiguate word senses. We utilize Wikip
 The evaluation shows that the approach is feasible; the word sense disambiguation achieves a recall of 0.799. Concerning topic labeling, in a user study subjects assessed that in 90.9% of the cases at least one proposed topic label among the first four is a good fit. With regard to precision, the subjects judged that 77.2% of the top ranked labels are a good fit or good but somewhat too broad (Fleiss' kappa κ = 0.27).
 
 ```
-(tbd)
+@article{weigeltWhat2020,
+  title = {What's the {{Matter}}? {{Knowledge Acquisition}} by {{Unsupervised Multi}}-{{Topic Labeling}} for {{Spoken Utterances}}},
+  shorttitle = {What's the {{Matter}}?},
+  author = {Weigelt, Sebastian and Keim, Jan and Hey, Tobias and Tichy, Walter F.},
+  year = {2020},
+  month = aug,
+  volume = {1},
+  pages = {43--66},
+  issn = {2641-953X},
+  doi = {10.35708/HCC1868-126364},
+  journal = {International Journal of Humanized Computing and Communication},
+  number = {1}
+}
 ```