@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications
%A Mueller, Mark-Christoph
%Y Kovatchev, Venelin
%Y Gold, Darina
%Y Zesch, Torsten
%S RELATIONS - Workshop on meaning relations between phrases and sentences
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F mueller-2019-semantic
%X 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.
%R 10.18653/v1/W19-0804
%U https://aclanthology.org/W19-0804/
%U https://doi.org/10.18653/v1/W19-0804
Markdown (Informal)
[Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications](https://aclanthology.org/W19-0804/) (Mueller, IWCS 2019)
ACL