Kathleen M. Carley

Also published as: Kathleen Carley


2019

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A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
Binxuan Huang | Kathleen Carley
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.

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Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Binxuan Huang | Kathleen Carley
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.

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Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations
Sumeet Kumar | Kathleen Carley
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.

2018

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Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Binxuan Huang | Kathleen Carley
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.

2016

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Relating semantic similarity and semantic association to how humans label other people
Kenneth Joseph | Kathleen M. Carley
Proceedings of the First Workshop on NLP and Computational Social Science