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Uncovering Probabilistic Implications in Typological Knowledge Bases ...
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Back to the Future -- Sequential Alignment of Text Representations ...
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Combining Sentiment Lexica with a Multi-View Variational Autoencoder ...
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Joint Emotion Label Space Modelling for Affect Lexica ...
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Abstract:
Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representation result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilising them. To combat this, we present an extended emotion lexicon of 30,273 unique entries, which is a result of merging eight existing emotion lexica by means of a multi-view variational autoencoder (VAE). We showed that a VAE is a valid approach for combining lexica with different label spaces into a joint emotion label space with a chosen number of dimensions, and that these dimensions are still interpretable. We tested the utility of the unified VAE lexicon by employing the lexicon values as features in an emotion detection model. We found that the VAE lexicon outperformed individual lexica, but contrary to our expectations, it did not outperform a naive concatenation of ... : Computer Speech and Language journal, to appear ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1911.08782 https://arxiv.org/abs/1911.08782
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Unsupervised Discovery of Gendered Language through Latent-Variable Modeling ...
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27 |
What Do Language Representations Really Represent?
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In: Bjerva, Johannes; Östling, Robert; Veiga, Maria Han; Tiedemann, Jörg; Augenstein, Isabelle (2019). What Do Language Representations Really Represent? Computational Linguistics, 45(2):381-389. (2019)
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On evaluating embedding models for knowledge base completion
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Specializing distributional vectors of all words for lexical entailment
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Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding ...
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Parameter sharing between dependency parsers for related languages ...
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From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings ...
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Learning distributional token representations from visual features
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Tracking Typological Traits of Uralic Languages in Distributed Language Representations ...
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Turing at SemEval-2017 task 8 : sequential approach to rumour stance classification with branch-LSTM
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