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      "text": "# Nagative Sampling\n\n- sentence to ngram\n- use as a sample if ngram is in anchors\n- positive if ngram is an anchor in the article of the sentence.\n- negative if ngram is an anchor in another article. (does not matter if it links to the same page or not.)\n- This is reasonable because on prod, we score (predict_proba) all to_titles of an anchor for the given from_article in anchors.\n- The links of the negative samples picked from anchors.\n- rbloom is used for `in` check to make it faster.\n- Negative correlation between the positive ratio and ambig (and freq) in wikis.\n- Therefore, small wikis have higher positive ratio, lower ambig and freq. (Root of the clustering idea for having less models.)",
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      "text": "sentence = \"a sentence in test\"\nanchors = \"filtered anchors in training\"\ndef process_sentence(sentence):\n        response = []\n        mentions = [ngram for ngram in get_ngrams(sentence, 1,5) if ngram in anchors]\n        for mention in mentions:\n                prediction = classify(mention)\n                response.add(prediction)\n        return response\n\ndef classify(mention):\n        # candidates are title_to \n        candidates = sorted(anchors[mention], by=\"frequency\")[:10]\n        predictions = []\n        for candidate in candidates:\n                features = generate_features(page, text, candidate_anchor)\n                prediction = model.predict(features)\n        return top prediction in predictions by predict_proba\n        \nprocess_sentence(sentence)",
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      "originalText": "sentence = \"a sentence in test\"\nanchors = \"filtered anchors in training\"\ndef process_sentence(sentence):\n        response = []\n        mentions = [ngram for ngram in get_ngrams(sentence, 1,5) if ngram in anchors]\n        for mention in mentions:\n                prediction = classify(mention)\n                response.add(prediction)\n        return response\n\ndef classify(mention):\n        # candidates are title_to \n        candidates = sorted(anchors[mention], by=\"frequency\")[:10]\n        predictions = []\n        for candidate in candidates:\n                features = generate_features(page, text, candidate_anchor)\n                prediction = model.predict(features)\n        return top prediction in predictions by predict_proba\n        \nprocess_sentence(sentence)",
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",
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",
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}