Laura Dietz


2021

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Learn The Big Picture: Representation Learning for Clustering
Sumanta Kashyapi | Laura Dietz
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Existing supervised models for text clustering find it difficult to directly optimize for clustering results. This is because clustering is a discrete process and it is difficult to estimate meaningful gradient of any discrete function that can drive gradient based optimization algorithms. So, existing supervised clustering algorithms indirectly optimize for some continuous function that approximates the clustering process. We propose a scalable training strategy that directly optimizes for a discrete clustering metric. We train a BERT-based embedding model using our method and evaluate it on two publicly available datasets. We show that our method outperforms another BERT-based embedding model employing Triplet loss and other unsupervised baselines. This suggests that optimizing directly for the clustering outcome indeed yields better representations suitable for clustering.

2019

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Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
Vivi Nastase | Benjamin Roth | Laura Dietz | Andrew McCallum
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

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An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications
Matthew Magnusson | Laura Dietz
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP: 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task.

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UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
Matthew Magnusson | Laura Dietz
Proceedings of the 13th International Workshop on Semantic Evaluation

The SemEval-2019 Task 12 is toponym resolution in scientific papers. We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document. We propose two methods: 1) sliding window convolutional neural network using ELMo embeddings (cnn-elmo), and 2) sliding window multi-Layer perceptron using ELMo embeddings (mlp-elmo). We also submit Bi-lateral LSTM with Conditional Random Fields (bi-LSTM) as a strong baseline given its state-of-art performance in Named Entity Recognition (NER) task. Our best performing model is cnn-elmo with a F1 of 0.844 which was below bi-LSTM F1 of 0.862 when evaluated on overlap macro detection. Eight teams participated in this subtask with a total of 21 submissions.

2015

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Image with a Message: Towards Detecting Non-Literal Image Usages by Visual Linking
Lydia Weiland | Laura Dietz | Simone Paolo Ponzetto
Proceedings of the Fourth Workshop on Vision and Language