One of these words is not like the other: a reproduction of outlier identification using non-contextual word representations

Jesper Brink Andersen, Mikkel Bak Bertelsen, Mikkel Hørby Schou, Manuel Rafael Ciosici, Ira Assent

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Word embeddings are an active topic in the NLP research community. State-of-the-art neural models achieve high performance on downstream tasks, albeit at the cost of computationally expensive training. Cost aware solutions require cheaper models that still achieve good performance. We present several reproduction studies of intrinsic evaluation tasks that evaluate non-contextual word representations in multiple languages.

Furthermore, we present 50-8-8, a new data set for the outlier identification task, which avoids limitations of the original data set, such as ambiguous words, infrequent words, and multi-word tokens, while increasing the number of test cases. The data set is expanded to contain semantic and syntactic tests and is multilingual (English, German, and Italian).

We provide an in-depth analysis of word embedding models with a range of hyper-parameters. Our analysis shows the suitability of different models and hyper-parameters for different tasks and the greater difficulty of representing German and Italian languages.
Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing and the 10th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Number of pages11
PublisherAssociation for Computational Linguistics
Publication dateNov 2020
Publication statusPublished - Nov 2020


  • Natural Language Processing
  • Word Embeddings
  • Intrinsic Evaluation
  • Outlier Identification
  • Multilingual Analysis


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