Papers by Stephen Wilson

4 papers
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text (2022.findings-emnlp)

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Challenge: 305 open access scientific articles are used for synthesis action graphs . lack of annotated data has hindered progress in this field .
Approach: They propose to annotate Polycrystalline Materials Synthesis Procedures PcMSP from 305 open access scientific articles for the construction of synthesis action graphs.
Outcome: The proposed dataset contains the synthesis sentences, entity mentions and intra-sentence relations extracted from the experimental paragraphs.
Automatic Evaluation of Local Topic Quality (P19-1)

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Challenge: Topic models are evaluated with global topic distributions but without local topic assignments.
Approach: They propose a task to elicit human judgments of token-level topic assignments . they propose to use global metrics to evaluate topic models at a local level .
Outcome: The proposed task elicits human judgments of token-level topic assignments . global metrics agree poorly with human assignments, the authors show .
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (D18-1)

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Challenge: Labeled Anchors is an interactive and supervised topic model based on the anchor words algorithm .
Approach: They propose an interactive supervised topic model based on the anchor words algorithm . they propose a classifier which requires no training beyond topic inference .
Outcome: The proposed model is human-interpretable and fast, and can be interactive.
Cross-referencing Using Fine-grained Topic Modeling (N19-1)

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Challenge: Cross-referencing is a useful study aid for facilitating comprehension of a text, but it requires extensive thematic knowledge and a focused search through the corpus to find such useful connections.
Approach: They propose a system for producing candidate cross-references which can be easily verified by human annotators.
Outcome: a new system can produce cross-references that can be easily verified by human annotators . the system uses fine-grained topic modeling to identify verse pairs which are topically related .

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