YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

Li, Tao; Lin, Lei; Choi, Mack Joong; Fu, King-Sun; Gong, Siyuan; Wang, Jian

doi:10.1109/isai-nlp.2018.8692799

Cited by 25 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In study [16], analysis on youtube"s comments has done where social media and politics has been focused to score selected comments by sentiment analysis. Authors have developed blog ranking algorithm in study [13] to find out the implicit and hidden links given in a blog based on content analysis.…”

Section: Related Workmentioning

confidence: 99%

1293 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E4804018520/2020©BEIESP DOI:10.35940/ijrte.E4804.018520 Journal Website: www.ijrte.org Muet Blogging: An Integral Platform for Students Projects

Mushtaque*,

Memon,

Bhatti

et al. 2020

IJRTE

View full text Add to dashboard Cite

Since the evolution of web 2.0 technologies, the use of weblogs has grown vastly. Weblog is a platform for discussion and awareness about various subjects. It has also promoted use of blogs in other IT sectors like educational blogs, fashion blogs, IT technology blogs, food recipe blogs, microblogs and other personalized blogs. The ratings of users on each post in a blog are significant. Researchers have suggested that using blogs in education are also vital as it instills awareness in the students and develops an interaction amongst them. Blogs are a subset of group-work as it diffuses awareness about different fields and technologies across the interested demographic. As students feel difficulty to go through all the previous projects and also find it difficult to select a particular domain for their final year projects. This research is the solution of above defined problem. In this research work, first a weblog for maintaining the final year B.E projects of MUET students is developed. This work embodies the feature of the post’s ranking at specific time with the help of a ranking algorithm that is applied on posts by MUET students. This algorithm is termed as WCA (Word CountAlgorithm) and uses the comments of users as its source. Developed ranking algorithm provide users particular information in several particular dimensions. The developed searching filters assists the weblog to announce a particular post as highest comments containing posts, 1 week older posts, 1 hour older posts, and all the older posts.Automated testing and user testing also proves the usefulness of the developed MUET blog

show abstract

Section: Related Workmentioning

confidence: 99%

1293 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E4804018520/2020©BEIESP DOI:10.35940/ijrte.E4804.018520 Journal Website: www.ijrte.org Muet Blogging: An Integral Platform for Students Projects

Mushtaque*,

Memon,

Bhatti

et al. 2020

IJRTE

View full text Add to dashboard Cite

show abstract

“…flips and cartwheels) and locomotion skills (walking and running) to train DL models for character motion animation instead of using motion capture systems. Reference [7] uses the video comments as a basis to create a sentiment analysis corpus for autonomous vehicles, [8] creates a video/audio corpus for lip reading training and [9] creates a corpus of 1.8 million 10sec audio clips for 632 audio event categories.…”

Section: Related Workmentioning

confidence: 99%

Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training

Drabeck¹,

Ramanan²,

Woo³

et al. 2020

IJMLC

View full text Add to dashboard Cite

For machine learning (ML) to work well, there is a need for large amounts of good quality training data. Obtaining such data is often the key bottleneck for the entire ML development process. Using humans to do explicit collection has been the main approach, but this tends to be expensive and time-consuming. Therefore, there is significant interest in creating alternative data collection techniques. We explore these alternative data collection techniques in the context of speech data in this paper. We were initially motivated by the problem of wake word engine training, where we need a large number of utterances for specific wake words. Given that there are already large public repositories of media data (e.g., YouTube, DailyMotion), we were curious as to how feasible it is to find the utterances that we need. Our results are encouraging as we found many different types of words can readily be found and downloaded in the quantity and quality needed to create training corpora for DL training. Usually > 30% of the found words are suitable for corpus creation. Greater than 80% of the top 10,000 ranks words and > 50% of the top 20,000 words we selected easily produced > 5000 found words, which is sufficient to train a high quality Wake Word Engine. Besides general words, we specifically looked for words used in wake word engine construction such as Name/Place/Product Name. Here, again, we find most common names/places/products return more than a sufficient number of words for corpus creation. Only uncommon names and places (like Atticus or Maximus) are difficult to find in sufficient quantities for corpus creation. We demonstrate a wake word engine trained from words we found in YouTube has the equivalent performance to one trained with traditional human collected words. Even though we were focused on wake words, our approach is general. It can be applied to create speech corpus for various purposes.

show abstract

“…We are excited about the idea of using GANs for text regression. Given the nature of the TR-GAN model, it is not challenging to find an experimental dataset; for example, [27] collected 50,000 textual comments below YouTube videos, among which 20,000 are labelled by state-of-the-art algorithms and 1,000 are labelled manually. We also are interested to see how the generated languages look like, given that existing literatures of using GANs for NLG merely report original experimental results but instead numerical metrics.…”

Section: Future Workmentioning

confidence: 99%

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Li²,

2018

2018 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting highlevel representations. Finally we point out related datasets for experiments and future research directions.

show abstract

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

Cited by 25 publications

References 24 publications

1293 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E4804018520/2020©BEIESP DOI:10.35940/ijrte.E4804.018520 Journal Website: www.ijrte.org Muet Blogging: An Integral Platform for Students Projects

1293 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E4804018520/2020©BEIESP DOI:10.35940/ijrte.E4804.018520 Journal Website: www.ijrte.org Muet Blogging: An Integral Platform for Students Projects

Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Contact Info

Product

Resources

About