A temporal model of text periodicities using Gaussian Processes
By Daniel Preotiuc-Pietro, Trevor Cohn. 2013
51 citations recorded by the source corpus.
Temporal variations of text are usually ignored in NLP applications. However, text use changes with time, which can affect many applications. In this paper we model periodic distributions of words over time. Focusing on hashtag frequency in Twitter, we first automatically identify the periodic patterns. We use this for regression in order to forecast the volume of a hashtag based on past data. We use Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel. We demonstrate this in a text classification setting, assigning the tweet hashtag based on the rest of its text. This method shows significant improvements over competitive baselines.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Lucia Specia
- Daniel Beck
- Carolina Scarton
- Yulia Tsvetkov
- Kashif Shah
- Chris Dyer
- Manaal Faruqui
- Ondrej Bojar
Citations in the corpus
- Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015, background): “All rights reserved. such regression problems in various domains, e.g. natural language processing (Cohn and Specia 2013), time series analysis (Preotiuc-Pietro and Cohn 2013), computer vision (Kapoor et al. 2009) and data mining (Lampos and Aletras 2014).”
- Word embeddings and discourse information for Quality Estimation (2016, methodology): “We also consider doing model selection by picking the model with highest likelihood on the training data, similar to the scheme used in (Preot¸iuc-Pietro and Cohn, 2013).”
- Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning (2016, methodology): “ian optimization has also received little attention in NLP. GPs were used in the task of machine translation quality estimation (Cohn and Specia, 2013) and in temporal analysis of social media texts (Preotiuc-Pietro and Cohn, 2013); TPEs were used by Yogatama et al. (2015) for optimizing choices of ”