2,545 research outputs found
Mining question-answer pairs from web forum: a survey of challenges and resolutions
Internet forums, which are also known as discussion boards, are popular web applications. Members of the board discuss issues and share ideas to form a community within the board, and as a result generate huge amount of content on different topics on daily basis. Interest in information extraction and knowledge discovery from such sources has been on the increase in the research community. A number of factors are limiting the potentiality of mining knowledge from forums. Lexical chasm or lexical gap that renders some Natural Language Processing techniques (NLP) less effective, Informal tone that creates noisy data, drifting of discussion topic that prevents focused mining and asynchronous issue that makes it difficult to establish post-reply relationship are some of the problems that need to be addressed. This survey introduces these challenges within the framework of question answering. The survey provides description of the problems; cites and explores useful publications to the reader for further examination; provides an overview of resolution strategies and findings relevant to the challenges
Punny Captions: Witty Wordplay in Image Descriptions
Wit is a form of rich interaction that is often grounded in a specific
situation (e.g., a comment in response to an event). In this work, we attempt
to build computational models that can produce witty descriptions for a given
image. Inspired by a cognitive account of humor appreciation, we employ
linguistic wordplay, specifically puns, in image descriptions. We develop two
approaches which involve retrieving witty descriptions for a given image from a
large corpus of sentences, or generating them via an encoder-decoder neural
network architecture. We compare our approach against meaningful baseline
approaches via human studies and show substantial improvements. We find that
when a human is subject to similar constraints as the model regarding word
usage and style, people vote the image descriptions generated by our model to
be slightly wittier than human-written witty descriptions. Unsurprisingly,
humans are almost always wittier than the model when they are free to choose
the vocabulary, style, etc.Comment: NAACL 2018 (11 pages
Towards a Unified Framework for Media Capacity Characterization: Inferences from Critical Analysis of Media Capacity Theories, Buzzwords and Web History
As the Web enters its third decade of existence, I draw attention to the need to better understand the Web as a potential reference case for how an information system transforms through incremental innovations, with particular focus on the Webâs advancement as a communication media platform. As a necessary research step in this quest, I critically examine whether one can use existing media capacity theories and media-related buzzwords (such as rich media, multimedia, hypermedia, social media) to characterize Web innovations as media. I examine and clarify these buzzwordsâ origins, meanings, and relationship with media capacity theories. I also elucidate discrepancies between them. Via inductive reasoning, I synthesize three media capacity dimensions (sensibility support, interactivity support and logistical support) as potential framework for objective media characterization. Each dimension could metamorphize into individual theories or one theory (e.g., sensibility interactivity and logistical support theory (SILST)). I present these dimensionsâ indicators and demonstrate three-dimensional typology of Web innovation milestones anchored on the three dimensionsâa step forward in substantiating the frameworkâs applicability to media capacity characterization
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Mobile-assisted language learning [Revised and updated version]
Mobile-assisted language learning (MALL) is the use of smartphones and other mobile technologies in language learning, especially in situations where portability and situated learning offer specific advantages. A key attraction of mobile learning is the ubiquity of mobile phones. Typical applications can support learners in reading, listening, speaking and writing in the target language, either individually or in collaboration with one another. Increasingly, MALL applications relate language learning to a personâs physical context when mobile, primarily to provide access to location-specific language material or to enable learners to capture aspects of language use in situ and share it with others. Mobile learning can be formal or informal, and mobile devices may form a bridge connecting in-class and out-of-class learning. When learning takes place outside the classroom, it is often beyond the reach and control of the teacher. This can be perceived as a threat, but it is also an opportunity to revitalize and rethink current approaches to teaching and learning. Mobile learning appeals to a wide range of people for a variety of reasons. It may exclude some learners but it is often a mechanism for inclusion. It is likely that the next generation of mobile learning will be more ubiquitous, which means that there will be smart systems everywhere for digital learning. Mobile learning is proving its potential to address authentic learner needs at the point at which they arise, and to deliver more flexible models of language learning
Middlewareâs message : the financial technics of codata
In this paper, I will argue for the relevance of certain distinctive features of messaging systems, namely those in which data (a) can be sent and received asynchronously, (b) can be sent to multiple simultaneous recipients and (c) is received as a âpotentially infiniteâ flow of unpredictable events. I will describe the social technology of the stock ticker, a telegraphic device introduced at the New York Stock Exchange in the 1860s, with reference to early twentieth century philosophers of synchronous experience (Bergson), simultaneous sign interpretations (Mead and Peirce), and flows of discrete events (Bachelard). Then, I will show how the tickerâs data flows developed into the 1990s-era technologies of message queues and message brokers, which distinguished themselves through their asynchronous implementation of ticker-like message feeds sent between otherwise incompatible computers and terminals. These latter systemsâ characteristic âpublish/subscribeâ communication pattern was one in which conceptually centralized (if logically distributed) flows of messages would be âpublished,â and for which âsubscribersâ would be spontaneously notified when events of interest occurred. This paradigmâcommon to the so-called âmessage-oriented middlewareâ systems of the late 1990sâwould re-emerge in different asynchronous distributed system contexts over the following decades, from âpush mediaâ to Twitter to the Internet of Things
Analyzing user reviews of messaging Apps for competitive analysis
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends
State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism
Overview
This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.
The paper is structured as follows:
Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS).
Part 2 provides an introduction to the key approaches of social media intelligence (henceforth âSOCMINTâ) for counter-terrorism.
Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored.
Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work
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