Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

WET: Word embedding-topic distribution vectors for MOOC video lectures dataset.

ABSTRACT: In this article, we present a dataset containing word embeddings and document topic distribution vectors generated from MOOCs video lecture transcripts. Transcripts of 12,032 video lectures from 200 courses were collected from Coursera learning platform. This large corpus of transcripts was used as input to two well-known NLP techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) to generate word embeddings and topic vectors, respectively. We used Word2Vec and LDA implementation in the Gensim package in Python. The data presented in this article are related to the research article entitled "Integrating word embeddings and document topics with deep learning in a video classification framework" [1]. The dataset is hosted in the Mendeley Data repository [2].

SUBMITTER: Kastrati Z

PROVIDER: S-EPMC6950834 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

WET: Word embedding-topic distribution vectors for MOOC video lectures dataset.

Kastrati Zenun Z Kurti Arianit A Imran Ali Shariq AS

Data in brief 20200103

In this article, we present a dataset containing word embeddings and document topic distribution vectors generated from MOOCs video lecture transcripts. Transcripts of 12,032 video lectures from 200 courses were collected from Coursera learning platform. This large corpus of transcripts was used as input to two well-known NLP techniques, namely Word2Vec and Latent Dirichlet Allocation (LDA) to generate word embeddings and topic vectors, respectively. We used Word2Vec and LDA implementation in th ...[more]

PMID: 31921958

Similar Datasets

Measuring novelty in science with word embedding.

Project description:Novelty is a core value in science, and a reliable measurement of novelty is crucial. This study proposes a new approach of measuring the novelty of scientific articles based on both citation data and text data. The proposed approach considers an article to be novel if it cites a combination of semantically distant references. To this end, we first assign a word embedding-a vector representation of each vocabulary-to each cited reference on the basis of text information included in the reference. With these vectors, a distance between every pair of references is computed. Finally, the novelty of a focal document is evaluated by summarizing the distances between all references. The approach draws on limited text information (the titles of references) and publicly shared library for word embeddings, which minimizes the requirement of data access and computational cost. We share the code, with which one can compute the novelty score of a document of interest only by having the focal document's reference list. We validate the proposed measure through three exercises. First, we confirm that word embeddings can be used to quantify semantic distances between documents by comparing with an established bibliometric distance measure. Second, we confirm the criterion-related validity of the proposed novelty measure with self-reported novelty scores collected from a questionnaire survey. Finally, as novelty is known to be correlated with future citation impact, we confirm that the proposed measure can predict future citation.

| S-EPMC8253414 | biostudies-literature

Observing the Testing Effect using Coursera Video-Recorded Lectures: A Preliminary Study.

Project description:We investigated the testing effect in Coursera video-based learning. One hundred and twenty-three participants either (a) studied an instructional video-recorded lecture four times, (b) studied the lecture three times and took one recall test, or (c) studied the lecture once and took three tests. They then took a final recall test, either immediately or a week later, through which their learning was assessed. Whereas repeated studying produced better recall performance than did repeated testing when the final test was administered immediately, testing produced better performance when the final test was delayed until a week after. The testing effect was observed using Coursera lectures. Future directions are documented.

| S-EPMC4731506 | biostudies-literature

Joint embedding VQA model based on dynamic word vector.

Project description:The existing joint embedding Visual Question Answering models use different combinations of image characterization, text characterization and feature fusion method, but all the existing models use static word vectors for text characterization. However, in the real language environment, the same word may represent different meanings in different contexts, and may also be used as different grammatical components. These differences cannot be effectively expressed by static word vectors, so there may be semantic and grammatical deviations. In order to solve this problem, our article constructs a joint embedding model based on dynamic word vector-none KB-Specific network (N-KBSN) model which is different from commonly used Visual Question Answering models based on static word vectors. The N-KBSN model consists of three main parts: question text and image feature extraction module, self attention and guided attention module, feature fusion and classifier module. Among them, the key parts of N-KBSN model are: image characterization based on Faster R-CNN, text characterization based on ELMo and feature enhancement based on multi-head attention mechanism. The experimental results show that the N-KBSN constructed in our experiment is better than the other 2017-winner (glove) model and 2019-winner (glove) model. The introduction of dynamic word vector improves the accuracy of the overall results.

| S-EPMC7959642 | biostudies-literature

FrameAxis: characterizing microframe bias and intensity with word embedding.

Project description:Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes") that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. Microframe bias captures how biased the text is on a certain microframe, and microframe intensity shows how prominently a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity align well with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news. The existing domain knowledge can be incorporated into FrameAxis by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument. Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines.

| S-EPMC8323720 | biostudies-literature

An annotated video dataset for computing video memorability.

Project description:Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.

| S-EPMC8661480 | biostudies-literature

Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts.

Project description:Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing similar surrounding words are semantically close. We argue that local contexts can only partially define word semantics in the unsupervised word embedding learning. Global contexts, referring to the broader semantic units, such as the document or paragraph where the word appears, can capture different aspects of word semantics and complement local contexts. We propose two simple yet effective unsupervised word embedding models that jointly model both local and global contexts to learn word representations. We provide theoretical interpretations of the proposed models to demonstrate how local and global contexts are jointly modeled, assuming a generative relationship between words and contexts. We conduct a thorough evaluation on a wide range of benchmark datasets. Our quantitative analysis and case study show that despite their simplicity, our two proposed models achieve superior performance on word similarity and text classification tasks.

| S-EPMC7931948 | biostudies-literature

word

Project description:word

| PRJEB52191 | ENA

A Neurology Clerkship Curriculum Using Video-Based Lectures and Just-in-Time Teaching (JiTT).

Project description:IntroductionJust-in-time teaching is an educational strategy that involves tailoring in-session learning activities based on student performance in presession assessments. We implemented this strategy in a third-year neurology clerkship.MethodsLinked to core neurology clerkship lectures, eight brief video-based lectures and knowledge assessments were developed. Students watched videos and completed multiple-choice questions, and results were provided to faculty, who were given the opportunity to adjust the in-person lecture accordingly. Feedback was obtained by surveys of students and faculty lecturers and from student focus groups and faculty. Student performance on the end-of-clerkship examination was analyzed.ResultsBetween October 2016 and April 2017, 135 students participated in the curriculum, and 56 students (41.5%) responded to the surveys. Most students agreed or strongly agreed that the new curriculum enhanced their learning and promoted their sense of responsibility in learning the content. Faculty agreed that this pedagogy helped prepare students for class. Most students watched the entire video-based lecture, although there was a trend toward decreased audience retention with longer lectures. There were no significant changes in performance on the end-of-clerkship examination after implementation of just-in-time teaching. In focus groups, students emphasized the importance of tying just-in-time teaching activities to the lecture and providing video-based lectures well in advance of the lectures.DiscussionJust-in-time teaching using video-based lectures is an acceptable and feasible method to augment learning during a neurology clinical clerkship. We believe this method could be used in other neurology clerkships with similar success.

| S-EPMC6342385 | biostudies-literature

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding.

Project description:In recent years, data-driven methods and artificial intelligence have been widely used in chemoinformatic and material informatics domains, for which the success is critically determined by the availability of training data with good quality and large quantity. A potential approach to break this bottleneck is by leveraging the chemical literature such as papers and patents as alternative data resources to high throughput experiments and simulation. Compared to other domains where natural language processing techniques have established successes, the chemical literature contains a large portion of phrases of multiple words that create additional challenges for accurate identification and representation. Here, we introduce a chemistry domain suitable approach to identify multiword chemical terms and train word representations at the phrase level. Through a series of special-designed experiments, we demonstrate that our multiword identifying and representing method effectively and accurately identifies multiword chemical terms from 119, 166 chemical patents and is more robust and precise to preserve the semantic meaning of chemical phrases compared to the conventional approach, which represents constituent single words first and combine them afterward. Because the accurate representation of chemical terms is the first and essential step to provide learning features for downstream natural language processing tasks, our results pave the road to utilize the large volume of chemical literature in future data-driven studies.

| S-EPMC6854573 | biostudies-literature

Incorporating Deep Learning With Word Embedding to Identify Plant Ubiquitylation Sites.

Project description:Protein ubiquitylation is an important posttranslational modification (PTM), which is involved in diverse biological processes and plays an essential role in the regulation of physiological mechanisms and diseases. The Protein Lysine Modifications Database (PLMD) has accumulated abundant ubiquitylated proteins with their substrate sites for more than 20 kinds of species. Numerous works have consequently developed a variety of ubiquitylation site prediction tools across all species, mainly relying on the predefined sequence features and machine learning algorithms. However, the difference in ubiquitylated patterns between these species stays unclear. In this work, the sequence-based characterization of ubiquitylated substrate sites has revealed remarkable differences among plants, animals, and fungi. Then an improved word-embedding scheme based on the transfer learning strategy was incorporated with the multilayer convolutional neural network (CNN) for identifying protein ubiquitylation sites. For the prediction of plant ubiquitylation sites, the proposed deep learning scheme could outperform the machine learning-based methods, with the accuracy of 75.6%, precision of 73.3%, recall of 76.7%, F-score of 0.7493, and 0.82 AUC on the independent testing set. Although the ubiquitylated specificity of substrate sites is complicated, this work has demonstrated that the application of the word-embedding method can enable the extraction of informative features and help the identification of ubiquitylated sites. To accelerate the investigation of protein ubiquitylation, the data sets and source code used in this study are freely available at https://github.com/wang-hong-fei/DL-plant-ubsites-prediction.

| S-EPMC7554246 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data