@inproceedings{mueller-2019-semantic,
    title = "Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications",
    author = "Mueller, Mark-Christoph",
    editor = "Kovatchev, Venelin  and
      Gold, Darina  and
      Zesch, Torsten",
    booktitle = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-0804/",
    doi = "10.18653/v1/W19-0804",
    abstract = "We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities."
}