サクサク読めて、アプリ限定の機能も多数!
トップへ戻る
Wikipedia
aylien.com
This is the first blog post in a two-part series. The series expands on the Frontiers of Natural Language Processing session organized by Herman Kamper and me at the Deep Learning Indaba 2018. Slides of the entire session can be found here. This post will discuss major recent advances in NLP focusing on neural network-based methods. The second post will discuss open problems in NLP. Disclaimer T
Probing models It was very refreshing to see that rather than introducing ever shinier new models, many papers methodically investigated existing models and what they capture. This was most commonly done by automatically creating a dataset that focuses on one particular aspect of the generalization behaviour and evaluating different trained models on this dataset: Conneau et al. for instance evalu
Brown clusters, an agglomerative, hierarchical clustering of word types based on contexts that was introduced in 1992 seem to come in vogue again. They were found to be particularly helpful for cross-lingual applications, while clusters were key features in several approaches: Mayhew et al. found that Brown cluster features were an important signal for cross-lingual NER. Botha et al. use word clus
I presented some preliminary work on using Generative Adversarial Networks to learn distributed representations of documents at the recent NIPS workshop on Adversarial Training. In this post I provide a brief overview of the paper and walk through some of the code. Learning document representations Representation learning has been a hot topic in recent years, in part driven by the desire to apply
There has been a large resurgence of interest in generative models recently (see this blog post by OpenAI for example). These are models that can learn to create data that is similar to data that we give them. The intuition behind this is that if we can get a model to write high-quality news articles for example, then it must have also learned a lot about news articles in general. Or in other word
News API Aggregate, understand, and deliver news content at scale.
Schedule your personalized demo with one of our NLP engineers What's covered in a demo? An engineer will show you how easy it is to identify and track stories and events as they unfold using our NLP powered platform.
このページを最初にブックマークしてみませんか?
『テキスト要約AYLIEN | We Bring Intelligent Tech』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く