• tf_idf_rank

  • features: TF, IDF, pos-tagging
  • filtering: by pos-tagging (such adj, conj...)
  • ranking: TF * IDF

• text_rank

  • features: word neighbors
  • filtering: by pos-tagging (such adj, conj...)
  • ranking: TextRank, which like PageRank, while building a relation matrix according to the words' positions.
  • reference: http://www.cse.unt.edu/~rada/papers/mihalcea.emnlp04.pdf

• glm_rank (TODO)

  • features: TF, IDF, pos-tagging, entity type, word position
  • ranking: train and predict by classification model

• semantic_rank (TODO)

  • features: such as TF, IDF, POS, entity type ...
  • first-ranking: classification model
  • re-ranking: adjust based on word co-occurence (Kobe and Oneal support each other) or topic model (whether words are belongs the main topics)

##Requirements:

• jieba: chinese word segmenter, https://github.com/fxsjy/jieba

• word2vec: for building word similarities, https://code.google.com/p/word2vec/