Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets

During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real-world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1805.0515

Research report on Bengali NLP engine for TTS

Includes bibliographical references (page 5).This report describes the Bengali NLP processor for TTS, along with the challenges faced in developing the NLP processor.Firoj Ala

CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.Comment: 9 page

Text to speech for Bangla language using festival

Includes bibliographical references (page 6-7).In this paper, we present a Text to Speech (TTS) synthesis system for Bangla language using the open-source Festival TTS engine. Festival is a complete TTS synthesis system, with components supporting front-end processing of the input text, language modeling, and speech synthesis using its signal processing module. The Bangla TTS system proposed here, creates the voice data for festival, and additionally extends festival using its embedded scheme scripting interface to incorporate Bangla language support. Festival is a oncatenative TTS system using diphone or other unit selection speech units. Our TTS implementation uses two different kinds of these concatenative methods supported in Festival: unit selection and multisyn unit selection. The function of a Text-to-Speech system is to convert some language text into its spoken equivalent by a series of modules. These modules, constituting the TTS system are described in detail which is very much helpful for future development. Finally, the quality of synthesized speech is assessed in terms of acceptability and intelligibility