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نتیجه جستجو - Word embeddings

تعداد مقالات یافته شده: 8
ردیف عنوان نوع
1 A framework for extracting urban functional regions based on multi prototype word embeddings using points-of-interest data
چارچوبی برای استخراج مناطق عملکردی شهری بر اساس تعبیه چند کلمه نمونه اولیه با استفاده از داده های مورد علاقه-2020
Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geodata. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a centercontext pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed.
Keywords: Urban functional regions | Word embeddings | Points-of-interest | Spatial clusters
مقاله انگلیسی
2 Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
مدل یادگیری عمیق برای تخمین پایان به پایان اندازه کاربردی COSMIC بر اساس نامهای مورد استفاده-2020
Context: COSMIC is a widely used functional size measurement (FSM) method that supports software development effort estimation. The FSM methods measure functional product size based on functional requirements. Unfortu- nately, when the description of the product’s functionality is often abstract or incomplete, the size of the product can only be approximated since the object to be measured is not yet fully described. Also, the measurement performed by human-experts can be time-consuming, therefore, it is worth considering automating it. Objective: Our objective is to design a new prediction model capable of approximating COSMIC-size of use cases based only on their names that is easier to train and more accurate than existing techniques. Method: Several neural-network architectures are investigated to build a COSMIC size approximation model. The accuracy of models is evaluated in a simulation study on the dataset of 437 use cases from 27 software develop- ment projects in the Management Information Systems (MIS) domain. The accuracy of the models is compared with the Average Use-Case approximation (AUC), and two recently proposed two-step models —Average Use-Case Goal-aware Approximation (AUCG) and Bayesian Network Use-Case Goal AproxImatioN (BN-UCGAIN). Results: The best prediction accuracy was obtained for a convolutional neural network using a word-embedding model trained on Wikipedia + Gigaworld. The accuracy of the model outperformed the baseline AUC model by ca. 20%, and the two-step models by ca. 5–7%. In the worst case, the improvement in the prediction accuracy is visible after estimating 10 use cases. Conclusions: The proposed deep learning model can be used to automatically approximate COSMIC size of software applications for which the requirements are documented in the form of use cases (or at least in the form of use- case names). The advantage of the model is that it does not require collecting historical data other than COSMIC size and names of use cases.
Keywords: Functional size approximation | Approximate software sizing methods | COSMIC | Deep learning | Word embeddings | Use cases
مقاله انگلیسی
3 Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities
توسعه یک خط لوله داده کاوی داده های توییتر در زمان واقعی در مقیاس ملی برای ژئو داده های اجتماعی در مورد اثرات احتمالی سیل بر جوامع-2019
Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/ emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20 and 29th June 2016.
Keywords: Flood management | Twitter | Volunteered geographic information | Natural language processing | Word embeddings | Social geodata
مقاله انگلیسی
4 Integrating word embeddings and document topics with deep learning in a video classification framework
تجمیع جاسازی کلمات و موضوعات مستند با یادگیری عمیق در یک چارچوب طبقه بندی ویدیو-2019
The advent of MOOC platforms brought an abundance of video educational content that made the selec- tion of best fitting content for a specific topic a lengthy process. To tackle this challenge in this paper we report our research efforts of using deep learning techniques for managing and classifying educational content for various search and retrieval applications in order to provide a more personalized learning experience. In this regard, we propose a framework which takes advantages of feature representations and deep learning for classifying video lectures in a MOOC setting to aid effective search and retrieval. The framework consists of three main modules. The first module called pre-processing concerns with video-to-text conversion. The second module is transcript representation which represents text in lecture transcripts into vector space by exploiting different representation techniques including bag-of-words, embeddings, transfer learning, and topic modeling. The final module covers classifiers whose aim is to la- bel video lectures into the appropriate categories. Two deep learning models, namely feed-forward deep neural network (DNN) and convolutional neural network (CNN) are examined as part of the classifier module. Multiple simulations are carried out on a large-scale real dataset using various feature represen- tations and classification techniques to test and validate the proposed framework.
Keywords: Deep learning | Video classification | Embedding | Document topics | CNN | DNN
مقاله انگلیسی
5 Collaborative filtering embeddings for memory-based recommender systems
تعبیه فیلترهای مشترک برای سیستمهای مبتنی بر حافظه-2019
Word embeddings techniques have attracted a lot of attention recently due to their effectiveness in different tasks. Inspired by the continuous bag-of-words model, we present prefs2vec, a novel embedding representation of users and items for memory-based recommender systems that rely solely on user–item preferences such as ratings. To improve the performance and prevent overfitting, we use a variant of dropout as regularization, which can leverage existent word2vec implementations. Additionally, we propose a procedure for incremental learning of embeddings that boosts the applicability of our proposal to production scenarios. The experiments show that prefs2vec with a standard memory-based recommender system outperforms all the state-of-the-art baselines in terms of ranking accuracy, diversity, and novelty.
Keywords: Embedding vector | User representation | Item representation | Collaborative filtering | Recommender systems
مقاله انگلیسی
6 Multi-sense embeddings through a word sense disambiguation process
توکاری چند حسه از طریق فرایند تفسیر کلمه حس-2019
Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA) , that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.
Keywords: Multi-sense | embeddings Natural language processing | Word similarity | Synset
مقاله انگلیسی
7 Neural word and entity embeddings for ad hoc retrieval
کلمه عصبی و جاگذاری های نهادی برای بازیابی تک موردی-2018
Learning low dimensional dense representations of the vocabularies of a corpus, known as neural embeddings, has gained much attention in the information retrieval community. While there have been several successful attempts at integrating embeddings within the ad hoc document retrieval task, yet, no systematic study has been reported that explores the various aspects of neural embeddings and how they impact retrieval performance. In this paper, we perform a methodical study on how neural embeddings influence the ad hoc document retrieval task. More specifically, we systematically explore the following research questions: (i) do methods solely based on neural embeddings perform competitively with state of the art retrieval methods with and without interpolation? (ii) are there any statistically significant difference between the performance of retrieval models when based on word embeddings compared to when knowledge graph entity embeddings are used? and (iii) is there significant difference between using locally trained neural embeddings compared to when globally trained neural embeddings are used? We examine these three research questions across both hard and all queries. Our study finds that word embeddings do not show competitive performance to any of the baselines. In contrast, entity embeddings show competitive performance to the baselines and when interpolated, outperform the best baselines for both hard and soft queries.
keywords: Neural embeddings| Ad hoc document retrieval| TREC| Knowledge graph
مقاله انگلیسی
8 شناسایی زبان خشونت آمیز (پرخاشگرانه) با استفاده از ویژگی‌ های جداساز (تعبیه شده) و احساسی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 15
این مقاله مشارکت ما را در اولین تکلیف مشترک در شناسایی پرخاشگری توصیف می‌کند. روش پیشنهادی متکی بر یادگیری ماشین برای شناسایی متن‌ های رسانه‌ای اجتماعی است که دارای پرخاشگری هستند. ویژگی ‌های اصلی مورد استفاده در روش ما اطلاعات استخراج ‌شده از کلمه جداساز و خروجی آنالیز احساسی می‌باشد. چندین روش یادگیری ماشین و ترکیب‌های مختلف ویژگی ‌ها امتحان شدند. ملاحظات رسمی از ماشین‌های بردار پشتیبان و جنگل‌های تصادفی استفاده کرد. ارزیابی رسمی نشان داد که برای متون مشابه آن‌هایی که در مجموعه داده آموزشی هستند، جنگل‌ها به بهترین نحو کار می‌کنند، در حالی که برای متونی که svmها متفاوت هستند انتخاب بهتری هستند. این ارزیابی همچنین نشان داد که با وجود سادگی روش، این روش در مقایسه با روش‌ های دقیقتر عملکرد خوبی دارد.
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