How to use the ngram.getBigram function in ngram

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github ChenglongChen / Kaggle_CrowdFlower / Code / Feat / genFeat_counting_feat.py View on Github external
    df["description_bigram"] = list(df.apply(lambda x: ngram.getBigram(x["description_unigram"], join_str), axis=1))
    ## trigram
github ChenglongChen / Kaggle_CrowdFlower / Code / Feat / genFeat_counting_feat.py View on Github external
    df["title_bigram"] = list(df.apply(lambda x: ngram.getBigram(x["title_unigram"], join_str), axis=1))
    df["description_bigram"] = list(df.apply(lambda x: ngram.getBigram(x["description_unigram"], join_str), axis=1))
github ChenglongChen / Kaggle_CrowdFlower / Code / Feat / genFeat_counting_feat.py View on Github external
    df["query_bigram"] = list(df.apply(lambda x: ngram.getBigram(x["query_unigram"], join_str), axis=1))
    df["title_bigram"] = list(df.apply(lambda x: ngram.getBigram(x["title_unigram"], join_str), axis=1))
github Cisco-Talos / fnc-1 / tree_model / generateFeatures.py View on Github external
        data["Headline_bigram"] = data["Headline_unigram"].map(lambda x: ngram.getBigram(x, join_str))
        data["articleBody_bigram"] = data["articleBody_unigram"].map(lambda x: ngram.getBigram(x, join_str))
github Cisco-Talos / fnc-1 / tree_model / generateFeatures.py View on Github external
        data["articleBody_bigram"] = data["articleBody_unigram"].map(lambda x: ngram.getBigram(x, join_str))