Steven Wilson,Walid Magdy,Barbara McGillivray,Kiran Garimella,Gareth Tyson
Abstract
The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.
- Anthology ID:
- 2020.lrec-1.586
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari,Frédéric Béchet,Philippe Blache,Khalid Choukri,Christopher Cieri,Thierry Declerck,Sara Goggi,Hitoshi Isahara,Bente Maegaard,Joseph Mariani,Hélène Mazo,Asuncion Moreno,Jan Odijk,Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4764–4773
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.586
- DOI:
- Bibkey:
- Cite (ACL):
- Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, and Gareth Tyson. 2020. Urban Dictionary Embeddings for Slang NLP Applications. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4764–4773, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Urban Dictionary Embeddings for Slang NLP Applications (Wilson et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.586.pdf
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@inproceedings{wilson-etal-2020-urban, title = "Urban Dictionary Embeddings for Slang {NLP} Applications", author = "Wilson, Steven and Magdy, Walid and McGillivray, Barbara and Garimella, Kiran and Tyson, Gareth", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.586", pages = "4764--4773", abstract = "The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.", language = "English", ISBN = "979-10-95546-34-4",}Download as File
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Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.</abstract> <identifier type="citekey">wilson-etal-2020-urban</identifier> <location> <url>https://aclanthology.org/2020.lrec-1.586</url> </location> <part> <date>2020-05</date> <extent unit="page"> <start>4764</start> <end>4773</end> </extent> </part></mods></modsCollection>
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%0 Conference Proceedings%T Urban Dictionary Embeddings for Slang NLP Applications%A Wilson, Steven%A Magdy, Walid%A McGillivray, Barbara%A Garimella, Kiran%A Tyson, Gareth%Y Calzolari, Nicoletta%Y Béchet, Frédéric%Y Blache, Philippe%Y Choukri, Khalid%Y Cieri, Christopher%Y Declerck, Thierry%Y Goggi, Sara%Y Isahara, Hitoshi%Y Maegaard, Bente%Y Mariani, Joseph%Y Mazo, Hélène%Y Moreno, Asuncion%Y Odijk, Jan%Y Piperidis, Stelios%S Proceedings of the Twelfth Language Resources and Evaluation Conference%D 2020%8 May%I European Language Resources Association%C Marseille, France%@ 979-10-95546-34-4%G English%F wilson-etal-2020-urban%X The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.%U https://aclanthology.org/2020.lrec-1.586%P 4764-4773
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Markdown (Informal)
[Urban Dictionary Embeddings for Slang NLP Applications](https://aclanthology.org/2020.lrec-1.586) (Wilson et al., LREC 2020)
- Urban Dictionary Embeddings for Slang NLP Applications (Wilson et al., LREC 2020)
ACL
- Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, and Gareth Tyson. 2020. Urban Dictionary Embeddings for Slang NLP Applications. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4764–4773, Marseille, France. European Language Resources Association.
