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{"gt_parse": {"c2t": "The block diagram represents a process flow for a text analysis system that involves data preprocessing, feature extraction, model training and validation, prediction, and evaluation. Here is a detailed description of the diagram:1. **Data Preprocessing**: This is the initial step where the raw data is prepared for analysis. It consists of two sub-steps: - **Strip white space**: This step involves removing any unnecessary white space from the text data. - **Uniform letter case**: This step standardizes the text data by converting all letters to a uniform case (either all uppercase or all lowercase).2. **Linguistic Feature Extraction**: After preprocessing, the next step is to extract linguistic features from the text data. This is divided into two parts: - **Part-of-speech tags (POS)**: This involves tagging each word with its corresponding part of speech (e.g., noun, verb, adjective). - **Named entity recognition (NER)**: This step identifies and classifies named entities (e.g., person names, organizations, locations) present in the text.3. **Model Training and Validation**: The extracted features are then used to train and validate different machine learning models. Two types of models are shown: - **BERT**: This is a pre-trained language model that can be fine-tuned for various natural language processing tasks. - **SVM**: Support Vector Machine is a traditional machine learning algorithm used for classification and regression tasks.4. **Prediction**: The trained models are used to make predictions on new, unseen data.5. **Evaluation**: The predictions made by the models are evaluated using various metrics to determine the best-performing model. The evaluation metrics shown are: - **ROC-AUC**: Receiver Operating Characteristic - Area Under Curve is a performance measurement for classification problems. - **Coverage error**: This metric measures how many labels on average must be included in the final prediction so that the true label is included. - **LRAP**: Label Ranking Average Precision is a metric for evaluating multi-label classification problems.6. **Best-performed**: This decision node checks if the current model's performance is the best among the ones tested. If the answer is Yes, the process moves back to the Prediction step, presumably to continue making predictions with the best-performing model. If the answer is No, the model is discarded.7. **Discard**: Models that do not perform the best are discarded.The diagram also shows that there are three types of input data for the evaluation step: - **Plain texts**: The original unprocessed text. - **POS**: Text data with part-of-speech tags. - **NER**: Text data with named entity recognition. - **POS+NER**: Text data with both part-of-speech tags and named entity recognition.This flowchart is typical for a machine learning pipeline where the goal is to train a model to understand and predict outcomes based on text data, and to select the best model based on evaluation metrics."}}