How Target Sense Verification improves domain-specific and enterprise disambiguation settings

Photo by Hu Chen on Unsplash

Dealing with ambiguous words has a long tradition in Natural Language Processing (NLP). However, in this fast-moving field, the original task formulations that solve this problem do not meet the requirements of modern domain-specific and enterprise settings.

In this post, we will take a deeper look at

  • the shortcomings of current Word Sense Disambiguation (WSD) and Entity Disambiguation (ED) task formulations

Disclaimer: This post is based on a recent publication at EACL 2021 titled…


The term word embedding has been floating around for years now, so most of you have most likely already had some contact point with it. This post of the series ML ED (Machine Learning, Easily Digestible) will focus on explaining the main principles behind this exciting methodology, like what is an embedding in general, what kind of information does it capture about a word, and what are points that need to be taken into account when deploying them. In the next part, we will take an overview of different kinds of word embedding models, exploring what their strengths and weaknesses…


When it comes to sharing knowledge with non-experts or out-of-domain people, researchers often fail to find a common language, which would be the basis for fruitful discussions and transfer of expertise. It indeed is a difficult balancing act, trying to abstract complex concepts, without losing scientific accuracy. Therefore, for the most part, one out of two strategies is chosen when trying to introduce non-experts to a topic.

One way is to stay at a very high level, only explaining the main theory, maybe some applications. Like that, the number of concepts that need to be explained is limited, and the…

Anna Breit

NLP and Graph ML Researcher, Computer Scientist, and Puzzle Enthusiast

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