Neural team recommendation has achieved state-of-the-art performance in forming teams of experts whose success in completing complex tasks is almost surely guaranteed.The proposed models frame the problem as a Boolean multilabel classification, mapping the dense vector representations of required skills to the sparse occurrence (multi-hot) vector representation of an optimum subset of experts using multilayer feedforward neural networks.Such approaches, however, suffer from the curse of sparsity in the highdimensional vector of optimum experts in the output layer.In this paper, we propose to reformulate the team recommendation problem into a sequence prediction task and leverage seq-to-seq models, including transformers, to map an input sequence of the required subset of skills onto an output sequence of the optimum subset of experts.Our experiments on four large-scale datasets from various domains, with distinct distributions of skills in teams, show that the seq-to-seq approach is consistently superior overall in a host of classification and information retrieval metrics.Our codebase is available at https://github.com/fani-lab/OpeNTF/tree/nmt.

Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.

The systematic linking of explicitly-observed phrases within a document to entities of a knowledge base has already been explored in a process known as entity linking. The objective of this paper, however, is to identify and entity link those entities that are not mentioned but are implied within a document, more specifically within a tweet. This process is referred to as implicit entity linking. Unlike prior work that build a representation for each entity based on its related content in the knowledge base, we propose to perform implicit entity linking by determining how a tweet is related to user-generated content posted online and as such indirectly perform entity linking. We formulate this problem as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of user-generated content related to the entities of the knowledge base. We systematically compare our work with the state-of-the-art baseline and show that our method is able to provide statistically significant improvements.

In this work, we automatically distinguish the efficient high elbow pose from dropping one in pulling phase of front crawl stroke in front view amateurly recorded videos. This task is challenging due to the aquatic environment and missing depth information. We predict the pull's efficiency through multiclass svm and random forest classifiers given arms key positions and angles as the feature set. We evaluate our approach over a labeled dataset of video frames taken from 25 members of masters' swim club at Ryerson University with different levels of expertise and physiological characteristics. Our results show the effectiveness of our approach with random forest classifier, yielding 67% accuracy.