Question: What keyphrase extraction models are explored?
Evidence: We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task BIBREF0 . We note that two of the systems are supervised and rely on the training set to build their classification models. Document frequency counts are also computed on the training set. Stemming is applied to allow more robust matching. The different keyphrase extraction models are briefly described below:
Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Table summarizes these numbers for each model. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to BIBREF16 .
Apart from TopicRank that groups similar candidates into topics, the other models do not have any redundancy control mechanism. Yet, recent work has shown that up to 12% of the overall error made by state-of-the-art keyphrase extraction systems were due to redundancy BIBREF6 , BIBREF17 . Therefore as a post-ranking step, we remove redundant keyphrases from the ranked lists generated by all models. A keyphrase is considered redundant if it is included in another keyphrase that is ranked higher in the list.
TF×IDF: we re-implemented the TF×IDF n-gram based baseline computed by the task organizers. We use 1, 2, 3-grams as keyphrase candidates and filter out those shorter than 3 characters, containing words made of only punctuation marks or one character long13 . Kea (Witten et al., 1999): keyphrase candidates are 1, 2, 3-grams that do not begin or end with a stop- word14. Keyphrases are selected using a na¨ıve bayes classifier15 with two features: TF×IDF and the relative position of first occurrence. TopicRank (Bougouin et al., 2013): keyphrase candidates are the longest sequences of adjacent nouns and adjectives. Lexically similar candidates are clustered into topics and ranked using Tex- tRank (Mihalcea and Tarau, 2004). Keyphrases are produced by extracting the first occurring candidate of the highest ranked topics. KP-Miner (El-Beltagy and Rafea, 2010): keyphrase candidates are sequences of words that do not contain punctuation marks or stopwords. Candidates that appear less than three times or that first occur beyond a certain position are removed16. Candidates are then weighted using a modified TF×IDF formula that account for document length. WINGNUS (Nguyen and Luong, 2010): keyphrase candidates are simplex nouns and noun phrases detected using a set of rules described in (Kim and Kan, 2009). Keyphrases are then selected using a na¨ıve bayes classifier with the optimal set of features found on the training set17: TF×IDF, relative position of first occurrence and candidate length in words.
Answer: TF×IDF,Kea (Witten et al., 1999), TopicRank (Bougouin et al., 2013),KP-Miner (El-Beltagy and Rafea, 2010), and WINGNUS (Nguyen and Luong, 2010).