@misc{niebler2017learning,
  abstract = {Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.},
  added-at = {2017-10-11T09:33:33.000+0200},
  author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas},
  biburl = {https://www.bibsonomy.org/bibtex/238ec3d4e41af2acda32f86c37ac89ad0/schwemmlein},
  description = {under review at ISWC 2017},
  interhash = {c7a3f26c61a1ac7b5789fbfb484b6a27},
  intrahash = {38ec3d4e41af2acda32f86c37ac89ad0},
  keywords = {learning metric relation semantic},
  note = {cite arxiv:1705.07425},
  timestamp = {2017-10-11T09:33:33.000+0200},
  title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  url = {http://arxiv.org/abs/1705.07425},
  year = 2017
}

@misc{niebler2017learning,
  abstract = {Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.},
  added-at = {2017-06-19T12:32:04.000+0200},
  author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas},
  biburl = {https://www.bibsonomy.org/bibtex/238ec3d4e41af2acda32f86c37ac89ad0/robertdjone},
  description = {under review at ISWC 2017},
  interhash = {c7a3f26c61a1ac7b5789fbfb484b6a27},
  intrahash = {38ec3d4e41af2acda32f86c37ac89ad0},
  keywords = {writting},
  note = {cite arxiv:1705.07425},
  timestamp = {2017-06-19T12:32:04.000+0200},
  title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  url = {http://arxiv.org/abs/1705.07425},
  year = 2017
}

@misc{niebler2017learning,
  abstract = {Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.},
  added-at = {2017-05-30T10:53:33.000+0200},
  author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas},
  biburl = {https://www.bibsonomy.org/bibtex/238ec3d4e41af2acda32f86c37ac89ad0/hotho},
  description = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  interhash = {c7a3f26c61a1ac7b5789fbfb484b6a27},
  intrahash = {38ec3d4e41af2acda32f86c37ac89ad0},
  keywords = {2017 learning metric myown relatedness semantic},
  note = {cite arxiv:1705.07425Comment: Under review at ISWC 2017},
  timestamp = {2017-05-30T10:53:33.000+0200},
  title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  url = {http://arxiv.org/abs/1705.07425},
  year = 2017
}

@misc{niebler2017learning,
  abstract = {Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.},
  added-at = {2017-05-24T03:10:04.000+0200},
  author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas},
  biburl = {https://www.bibsonomy.org/bibtex/238ec3d4e41af2acda32f86c37ac89ad0/dmir},
  description = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  interhash = {c7a3f26c61a1ac7b5789fbfb484b6a27},
  intrahash = {38ec3d4e41af2acda32f86c37ac89ad0},
  keywords = {from:thoni learning metric myown posts postsii relatedness research_representation_learning semantic},
  note = {cite arxiv:1705.07425},
  timestamp = {2024-05-16T13:36:26.000+0200},
  title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  url = {http://arxiv.org/abs/1705.07425},
  year = 2017
}

@article{niebler2017learning,
  abstract = {Assessing the degree of semantic relatedness between words is an important
task with a variety of semantic applications, such as ontology learning for the
Semantic Web, semantic search or query expansion. To accomplish this in an
automated fashion, many relatedness measures have been proposed. However, most
of these metrics only encode information contained in the underlying corpus and
thus do not directly model human intuition. To solve this, we propose to
utilize a metric learning approach to improve existing semantic relatedness
measures by learning from additional information, such as explicit human
feedback. For this, we argue to use word embeddings instead of traditional
high-dimensional vector representations in order to leverage their semantic
density and to reduce computational cost. We rigorously test our approach on
several domains including tagging data as well as publicly available embeddings
based on Wikipedia texts and navigation. Human feedback about semantic
relatedness for learning and evaluation is extracted from publicly available
datasets such as MEN or WS-353. We find that our method can significantly
improve semantic relatedness measures by learning from additional information,
such as explicit human feedback. For tagging data, we are the first to generate
and study embeddings. Our results are of special interest for ontology and
recommendation engineers, but also for any other researchers and practitioners
of Semantic Web techniques.},
  added-at = {2017-05-23T14:31:08.000+0200},
  author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas},
  author+an = {1=highlight},
  biburl = {https://www.bibsonomy.org/bibtex/2c39dd7b533b4ca80066f58074df2f1c7/thoni},
  interhash = {c7a3f26c61a1ac7b5789fbfb484b6a27},
  intrahash = {c39dd7b533b4ca80066f58074df2f1c7},
  journal = {arXiv preprint arXiv:1705.07425},
  keywords = {learning metric myown posts postsii published relatedness selected semantic},
  note = {cite arxiv: 1705.07425},
  timestamp = {2020-05-06T19:17:45.000+0200},
  title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning},
  type = {Publication},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1705.html#Niebler0PH17},
  year = 2017
}