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Publications

The Iris.ai research team works with a combination of in-house research and external libraries.

Here are our own publications.

  1. [Date] Nov 2021
    [Title] Domain-adaptation of spherical embeddings
    [URL] https://arxiv.org/abs/2111.00677
    [Summary] The recent spherical embedding model (JoSE) proposed in arXiv:1911.01196 jointly learns word and document embeddings during training on the multi-dimensional unit sphere, which performs well for document classification and word correlation tasks. But, we show a non-convergence caused by global rotations during its training prevents it from domain adaptation.

  2. [Date] May 2018
    [Title] Scithon™ – An evaluation framework for assessing research productivity tools
    [URL] http://lrec-conf.org/workshops/lrec2018/W24/pdf/7_W24.pdf
    [Summary] We develop the novel framework, Scithon™, for performing evaluation tasks on the science discovery tools. The framework, organized around live events, involves a systematic and cross-disciplinary comparison that focuses on productivity gains and takes into account user engagement.

  3. [Date] 15 December 2017
    [Title] Word importance-based similarity of documents metric (WISDM): Fast and scalable document similarity metric for analysis of scientific documents
    [URL] https://doi.org/10.1145/3127526.3127530
    [Summary] Word importance-based similarity of documents metric (WISDM) is a fast and scalable novel method for document similarity/distance computation for analysis of scientific documents. It is based on recent advancements in the area of word embeddings and it achieves significant performance speed-up in comparison to state-of-the-art metrics with a very marginal drop in precision.
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