Journal of Interesting Negative Results in Natural Language Processing and Machine Learning

JINR (ISSN 1916-7423) is an electronic journal, with a printed version to be negotiated with a major publisher once we have established a steady presence. The journal will bring to the fore research in Natural Language Processing and Machine Learning that uncovers interesting negative results. It is becoming more and more obvious that the research community in general, and those who work NLP and ML in particular, are biased towards publishing successful ideas and experiments. Insofar as both our research areas focus on theories "proven" via empirical methods, we are sure to encounter ideas that fail at the experimental stage for unexpected, and often interesting, reasons. Much can be learned by analysing why some ideas, while intuitive and plausible, do not work. The importance of counter-examples for disproving conjectures is already well known. Negative results may point to interesting and important open problems. Knowing directions that lead to dead-ends in research can help others avoid replicating paths that take them nowhere. This might accelerate progress or even break through walls!