On December 17-19, Bjørnar Tessem presented a new paper about analogical reasoning about news angles based on text similarity at the 39th SGAI International Conference on Artificial Intelligence (AI 2019) in Cambridge, UK.
Tessem, Bjørnar (2019). Analogical New Angles from Text Similarity. In Artificial Intelligence XXXVI, Lecture Notes in Computer Science (LNCS), Springer, pp. 449-455.
Tareq Al-Moslmi presents three papers on named-entity recognition and cross-domain sentiment analysis at the First International Conference of Intelligent Computing and Engineering (ICOICE 2019) in Yemen, one of them co-authored with Marc Gallofré-Ocaña.
Albared, M., Ocaña, M. G., Ghareb, A., & Al-Moslmi, T. (2019, December). Recent Progress of Named Entity Recognition over the Most Popular Datasets. In 2019 First International Conference of Intelligent Computing and Engineering (ICOICE) (pp. 1-9). IEEE.
Al-Moslmi, T., Albared, M., Al-Shabi, A., & Abdullah, S. (2019, December). Bidirectional Feature Transfer for Cross-Domain Sentiment Analysis. In 2019 First International Conference of Intelligent Computing and Engineering (ICOICE) (pp. 1-8). IEEE.
Al-Moslmi, T., Albared, M., Al-Shabi, A., Abdullah, S., & Omar, N. (2019, December). A Comparative Study Of Co-Occurrence Strategies for Building A Cross-Domain Sentiment Thesaurus. In 2019 First International Conference of Intelligent Computing and Engineering (ICOICE) (pp. 1-8). IEEE.
At the Australasian Joint Conference on Artificial Intelligence (AI 2019), Csaba Veres presented his joint paper on A Machine Learning Benchmark with Meaning: Learnability and Verb Semantics.
Abstract: Just over thirty years ago the prospect of modelling human knowledge with parallel distributed processing systems without explicit rules, became a possibility. In the past five years we have seen remarkable progress with artificial neural network (ANN) based systems being able to solve previously difficult problems in many cognitive domains. With a focus on Natural Language Processing (NLP), we argue that the progress is in part illusory because the benchmarks that measure progress have become task oriented, and have lost sight of the goal to model knowledge. Task oriented benchmarks are not informative about the reasons machine learning succeeds, or fails. We propose a new dataset in which the correct answers to entailments and grammaticality judgements depend crucially on specific items of knowledge about verb semantics, and therefore errors on performance can be directly traced to deficiencies in knowledge. If this knowledge is not learnable from the provided input, then it must be provided as an innate prior.
Veres C., Sandblåst B.H. (2019) A Machine Learning Benchmark with Meaning: Learnability and Verb Semantics. In: Liu J., Bailey J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science, vol 11919. Springer, Cham.