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Showing 1 to 4 of 4 for “"Sentence Classification"”.

  1. Sequential short-text classification with neural networks

    … reviews. In particular, we focus on short-text classification, to help authors of systematic reviews locate the desired information. We introduce several algorithms to perform sequential short-text classification, which outperform state-of-the-art algorithms. To facilitate the choice of …

    mit Repository record for Sequential short-text classification with neural networks (opens in a new tab)

  2. Annotating and Automatically Extracting Task Descriptions from Shared Task Overview Papers in Natural Language Processing Domains

    … comprises 254 full text papers containing 41,752 sentences and 259 task descriptions. In our second and final validation we achieved a strict score of 0.44 and a relaxed score of 0.95, measured using Cohen's kappa coefficent. We then used this resource to facilitate the training and development of …

    umn Repository record for Annotating and Automatically Extracting Task Descriptions from Shared Task Overview Papers in Natural Language Processing Domains (opens in a new tab)

  3. Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration

    … relations? The analysis measures the quality of sentence classification using term TF-IDF representations, and finds a practical upper limit to precision and recall in a biomedical text classification task (F1-score of 0.85). Arguably, one could use ontologies to supplement TF-IDF, but ontologies …

    vt Repository record for Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration (opens in a new tab)

  4. Scholarly Information System for Long Documents and their Elements: Structured Representation and Exploration using Knowledge Graphs and LLMs

    … pieces like captions, figures, and key sentences, so they can be searched directly. It also builds a structured map, called a knowledge graph, that shows how these pieces relate to each other. This allows the system to understand the document more like a human would. Using this …

    vt Repository record for Scholarly Information System for Long Documents and their Elements: Structured Representation and Exploration using Knowledge Graphs and LLMs (opens in a new tab)