9722
This paper proposes a Hidden Markov Model -LRB- HMM -RRB- and an HMM-based chunk tagger , from which a named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system is built to recognize and classify names , times and numerical quantities .
 Hidden Markov Model -LRB- HMM -RRB-, HMM-based chunk tagger; Hidden Markov Model -LRB- HMM -RRB-, named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system; HMM-based chunk tagger, named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system; named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system, names; named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system, times and numerical quantities; names, times and numerical quantities
Please list all entity pairs containing a certain relationship in the given options.Output format is "word1, word2; word3, word4". 
Option: conjunction, feature of, hyponym of, used for, part of, compare, evaluate for 
Text: {0} 
Answer:
 Hidden Markov Model -LRB- HMM -RRB-, HMM-based chunk tagger; Hidden Markov Model -LRB- HMM -RRB-, named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system; HMM-based chunk tagger, named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system; named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system, names; named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system, times and numerical quantities; names, times and numerical quantities