ETGP: Top-K Geography-Text P/S Approach without Threshold

Social media are more and more popular. Subsequently, geography-text data has caused wide attention. Different from the traditional publish/subscribe (P/S), geography-text data is published and subscribed in the form of dynamic data flow in the mobile network. The difference raises higher demands for facility. However, previous top-k geography-text P/S approaches want to set a set of thresholds. A user should take time to set a threshold for each subscription, which is not facile enough. The threshold yields many weaknesses to users. Therefore, we herein propose an efficient top-k geography-text P/S approach that excludes the threshold, called ETGP. Our approach does not need users to set any threshold. Subsequently, the ETGP returns the highest score results to the subscriber without setting a threshold. Therefore, our approach can lessen redundant computations, promote the query integrity rate, and make P/S system easier for the user to use. Comprehensive experiments prove the efficiency of the proposed approach with high facility

TA-COS 2016 : First workshop on text analytics for cybersecurity and online safety : Proceedings

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Large Language Models for Software Engineering: A Systematic Literature Review

Large Language Models (LLMs) have significantly impacted numerous domains, notably including Software Engineering (SE). Nevertheless, a well-rounded understanding of the application, effects, and possible limitations of LLMs within SE is still in its early stages. To bridge this gap, our systematic literature review takes a deep dive into the intersection of LLMs and SE, with a particular focus on understanding how LLMs can be exploited in SE to optimize processes and outcomes. Through a comprehensive review approach, we collect and analyze a total of 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize and provide a comparative analysis of different LLMs that have been employed in SE tasks, laying out their distinctive features and uses. For RQ2, we detail the methods involved in data collection, preprocessing, and application in this realm, shedding light on the critical role of robust, well-curated datasets for successful LLM implementation. RQ3 allows us to examine the specific SE tasks where LLMs have shown remarkable success, illuminating their practical contributions to the field. Finally, RQ4 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE, as well as the common techniques related to prompt optimization. Armed with insights drawn from addressing the aforementioned RQs, we sketch a picture of the current state-of-the-art, pinpointing trends, identifying gaps in existing research, and flagging promising areas for future study

Lightweight Spoken Utterance Classification with CFG, tf-idf and Dynamic Programming

We describe a simple spoken utterance classification method suitable for data-sparse domains which can be approximately described by CFG grammars. The central idea is to perform robust matching of CFG rules against output from a large-vocabulary recogniser, using a dynamic programming method which optimises the tf-idf score of the matched grammar string. We present results of experiments carried out on a substantial CFG-based medical speech translator and the publicly available Spoken CALL Shared Task. Robust utterance classification using the tf-idf method strongly outperforms plain CFG-based recognition for both domains. When comparing with Naive Bayes classifiers trained on data sampled from the CFG grammars, the tf-idf/dynamic programming method is much better on the complex speech translation domain, but worse on the simple Spoken CALL Shared Task domain

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal