Papers by Peter Szolovits
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (D18-1)
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| Challenge: | Existing sentences classification models often classify sentences in isolation without considering the context in which sentences appear. |
| Approach: | They propose a hierarchical sequential labeling network to make use of contextual information within surrounding sentences to help classify the current sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts. |
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)
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| Challenge: | Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles. |
| Approach: | They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework . |
| Outcome: | The proposed method outperforms the state-of-the-art summarization model by 9.68% . it can generate relevant, fluent headlines with humor, romance and clickbait . |
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text (D19-62)
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| Challenge: | Existing evidence for improved performance on natural language tasks is unclear to what degree the learned token representations capture and encode highlevel morphological/syntactic knowledge about the usage of a given token in a sentence. |
| Approach: | They propose to use context-aware token representations to capture morphological/syntactic knowledge about the usage of a given word/token in a sentence. |
| Outcome: | The proposed representations capture and encode high-level morphological/syntactic knowledge about the usage of a given token in a sentence. |
Transfer Learning for Named-Entity Recognition with Neural Networks (L18-1)
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| Challenge: | Existing approaches to named-entity recognition (NER) require additional lead time for developing and fine-tuning the rules. |
| Approach: | They propose to transfer an ANN model trained on a large labeled dataset to another dataset with a limited number of labels to improve upon the state-of-the-art results for patient note de-identification. |
| Outcome: | The proposed model can be transferred to a dataset with a limited number of labels, and improves on the state-of-the-art results on patient note de-identification. |