Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai,Supriya Kapoor,Rahul Bhatia Conference proceedings 2021 Springer Natu

BUST

Classification Based Method for Disfluencies Detection in Spontaneous Spoken Tunisian Dialect,ly detecting disfluencies in spontaneous spoken Tunisian dialect. Our method uses a classification model based on a sequence-tagging approach with purely linguistic features for detecting disfluent segments of the utterance. According to our study of the Tunisian dialect, we have identified eight ty

MAG

A Comprehensive Methodology for Evaluating Conversation-Based Interfaces to Relational Databases (Con this output using one or more evaluation measures in which a careful inspection is performed. This paper presents a review of evaluation techniques for Conversational Agents (CAs) and Natural Language Interfaces to Databases (NLIDBs). It then introduces the developed customized evaluation methodo

形状

Disease Normalization with Graph Embeddings,dentified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH.. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeS

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Commentary

Charitable

Adaptive Attention Mechanism Based Semantic Compositional Network for Video Captioning,, 2) we combine two levels of LSTM with temporal attention mechanism and adaptive attention mechanism respectively. Then we propose an adaptive attention mechanism based semantic compositional network (AASCNet) for video captioning. Specifically, the framework uses temporal attention mechanism to se

SLING

Food Classification for Inflammation Recognition Through Ingredient Label Analysis: A Real NLP Case in order to solve this task on real commercial products, aiming to create a baseline for future works and a software-product. In the end, interesting and noticeable results have been achieved and the baselines have been identified into the Linear SVM and the Dense NN with Bag of Words or with the c

出汗

agnostic

Quranic Topic Modelling Using Paragraph Vectors,nce to our conclusions. Using the paragraph vectors model, we managed to generate a document embedding space that model and explain word distribution in the Holy Quran. The dimensions in the space represent the semantic structure in the data and ultimately help to identify main topics and concepts i