Semantic Word Segmentation in Tag-line Search

Semantic Word Segmentation in Tag-line Search – Word embeddings are an important statistical tool in many applications including human-computer interaction and natural language processing systems. In this work, we show that one-way word embeddings enable semantic segmentation of multiple words, and that this segmentation results in the segmentation of phrases with multiple entities that were not considered previously in the word embeddings. To this end, we propose a novel approach for this task, which leverages the semantic word embeddings. Our experimental results show that our model outperforms state-of-the-art approaches by a large margin on various benchmarks.

Most medical applications require automated clinical diagnosis. In this work, we show how clinical applications can generate customized diagnosis models based on medical data. Our model is based on the concept of personalized data, which is a fundamental part of clinical applications. We show that such a machine learning model can, by learning the human patient characteristics, learn diagnoses from data that are relevant to the patients’ condition. We further show how these medical diagnoses could be extracted by a machine learning model which uses the patient characteristics of the patients as well as the patient characteristics of the patients. The model has the ability to adapt the patient characteristics to the data, using a specific patient description for patients and the classification of the patients’ status using the human patients. This model can also be used to automatically process the patient characteristics as a whole instead of just their diagnosis.

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Semantic Word Segmentation in Tag-line Search

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  • Explanation-based analysis of taxonomic information in taxonomical text

    Towards the Collaborative Training of Automated Cardiac Diagnosis ModelsMost medical applications require automated clinical diagnosis. In this work, we show how clinical applications can generate customized diagnosis models based on medical data. Our model is based on the concept of personalized data, which is a fundamental part of clinical applications. We show that such a machine learning model can, by learning the human patient characteristics, learn diagnoses from data that are relevant to the patients’ condition. We further show how these medical diagnoses could be extracted by a machine learning model which uses the patient characteristics of the patients as well as the patient characteristics of the patients. The model has the ability to adapt the patient characteristics to the data, using a specific patient description for patients and the classification of the patients’ status using the human patients. This model can also be used to automatically process the patient characteristics as a whole instead of just their diagnosis.


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