A review on the basketball jump shot

The ability to shoot an effective jump shot in the sport of basketball is critical to a player's success. In an attempt to better understand the aspects related to expert performance, researchers have investigated successful free throws and jump shots of various basketball players and identified movement variables that contribute to their success. The purpose of this study was to complete a systematic review of the scientific literature on the basketball free throw and jump shot for the purpose of revealing the critical components of shooting that coaches, teachers, and players should focus on when teaching, learning, practising, and performing a jump shot. The results of this review are presented in three sections: (a) variables that affect ball trajectory, (b) phases of the jump shot, and (c) additional variables that influence shooting.     

Named entity recognition with multiple segment representations

Named entity recognition (NER) is mostly formalized as a sequence labeling problem in which segments of named entities are represented by label sequences. Although a considerable effort has been made to investigate sophisticated features that encode textual characteristics of named entities (e.g. PEOPLE, LOCATION, etc.), little attention has been paid to segment representations (SRs) for multi-token named entities (e.g. the IOB2 notation). In this paper, we investigate the effects of different SRs on NER tasks, and propose a feature generation method using multiple SRs. The proposed method allows a model to exploit not only highly discriminative features of complex SRs but also robust features of simple SRs against the data sparseness problem. Since it incorporates different SRs as feature functions of Conditional Random Fields (CRFs), we can use the well-established procedure for training. In addition, the tagging speed of a model integrating multiple SRs can be accelerated equivalent to that of a model using only the most complex SR of the integrated model. Experimental results demonstrate that incorporating multiple SRs into a single model improves the performance and the stability of NER. We also provide the detailed analysis of the results.     

A bottom-up approach to sentence ordering for multi-document summarization

Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.