Review of one NLP paper each day
Podcast Overview
Review of one NLP paper each day
Language
🇺🇲
Publishing Since
1/31/2021
1 verified contact email on file for A Paper A Day
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
February 3, 2021
WordNet: A Lexical Database for English
<p>WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. It is a computational lexicon of English based on psycholinguistic principles, created and maintained at Princeton University.</p> <p>The main relation among words in WordNet is synonymy, as between the words shut and close or car and automobile. Synonyms--words that denote the same concept and are interchangeable in many contexts--are grouped into unordered sets (synsets).</p> <p>Link to official page: https://wordnet.princeton.edu/</p> <p>Citation: Princeton University "About WordNet." WordNet. Princeton University. 2010.</p> <p><br></p> <p><br></p>
February 2, 2021
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
<p>Link to paper: https://arxiv.org/pdf/1908.07245.pdf</p> <p>Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.</p> <p><br></p>
January 31, 2021
Introduction
<p>Introduction on my background and what this podcast will be about.</p>
3 total episodes available
Deep-dive analytics for A Paper A Day
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is A Paper A Day?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.