Search Results for author:
Found 24 papers, 7 papers with code
User Factor Adaptation for User Embedding via Multitask Learning
In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI).
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.
Evaluating Topic Quality with Posterior Variability
Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters.
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019
We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users.
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations.
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space.
Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
Language use varies across different demographic factors, such as gender, age, and geographic location.
Analyzing Bayesian Crosslingual Transfer in Topic Models
We introduce a theoretical analysis of crosslingual transfer in probabilistic topic models.
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models
Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features.
Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018
The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets.
Learning Multilingual Topics from Incomparable Corpora
Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora.
Learning Multilingual Topics from Incomparable Corpus
Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora.
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation
Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data.
Incorporating Metadata into Content-Based User Embeddings
Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference.
Feature Selection as Causal Inference: Experiments with Text Classification
This paper proposes a matching technique for learning causal associations between word features and class labels in document classification.
Sprite: Generalizing Topic Models with Structured Priors
We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components.
