1 import nltk
2 passage = """After the Lion Air crash, questions were raised, so Boeing sent further instructions that it said pilots should know,” he said, according to the Associated Press. “Those relate to the specific behavior of this specific type of aircraft. As a result, training was given by Boeing, and our pilots have taken it and put it into our manuals."""
3 sentences = nltk.sent_tokenize( passage )
4 for sent in sentences:
5     tokens = nltk.word_tokenize( sent )
6     posTags = nltk.pos_tag( tokens )
7     print( posTags )

[('After', 'IN'), ('the', 'DT'), ('Lion', 'NNP'), ('Air', 'NNP'), ('crash', 'NN'), (',', ','), ('questions', 'NNS'), ('were', 'VBD'), ('raised', 'VBN'), (',', ','), ('so', 'IN'), ('Boeing', 'NNP'), ('sent', 'VBD'), ('further', 'JJ'), ('instructions', 'NNS'), ('that', 'IN'), ('it', 'PRP'), ('said', 'VBD'), ('pilots', 'NNS'), ('should', 'MD'), ('know', 'VB'), (',', ','), ('”', 'FW'), ('he', 'PRP'), ('said', 'VBD'), (',', ','), ('according', 'VBG'), ('to', 'TO'), ('the', 'DT'), ('Associated', 'NNP'), ('Press', 'NNP'), ('.', '.')]
[('“Those', 'JJ'), ('relate', 'NN'), ('to', 'TO'), ('the', 'DT'), ('specific', 'JJ'), ('behavior', 'NN'), ('of', 'IN'), ('this', 'DT'), ('specific', 'JJ'), ('type', 'NN'), ('of', 'IN'), ('aircraft', 'NN'), ('.', '.')]
[('As', 'IN'), ('a', 'DT'), ('result', 'NN'), (',', ','), ('training', 'NN'), ('was', 'VBD'), ('given', 'VBN'), ('by', 'IN'), ('Boeing', 'NNP'), (',', ','), ('and', 'CC'), ('our', 'PRP$'), ('pilots', 'NNS'), ('have', 'VBP'), ('taken', 'VBN'), ('it', 'PRP'), ('and', 'CC'), ('put', 'VB'), ('it', 'PRP'), ('into', 'IN'), ('our', 'PRP$'), ('manuals', 'NNS'), ('.', '.')]

1 import nltk
2 passage = """After the Lion Air crash, questions were raised, so Boeing sent further instructions that it said pilots should know,” he said, according to the Associated Press. “Those relate to the specific behavior of this specific type of aircraft. As a result, training was given by Boeing, and our pilots have taken it and put it into our manuals."""
3 sentences = nltk.sent_tokenize( passage )
4 for sent in sentences:
5     tokens = nltk.word_tokenize( sent )
6     posTags = nltk.pos_tag( tokens )
7     for tag in posTags:
8         print( "{}({}) ".format( tag[0], tag[1] ), end = "" )

After(IN) the(DT) Lion(NNP) Air(NNP) crash(NN) ,(,) questions(NNS) were(VBD) raised(VBN) ,(,) so(IN) Boeing(NNP) sent(VBD) further(JJ) instructions(NNS) that(IN) it(PRP) said(VBD) pilots(NNS) should(MD) know(VB) ,(,) ”(FW) he(PRP) said(VBD) ,(,) according(VBG) to(TO) the(DT) Associated(NNP) Press(NNP) .(.) “Those(JJ) relate(NN) to(TO) the(DT) specific(JJ) behavior(NN) of(IN) this(DT) specific(JJ) type(NN) of(IN) aircraft(NN) .(.) As(IN) a(DT) result(NN) ,(,) training(NN) was(VBD) given(VBN) by(IN) Boeing(NNP) ,(,) and(CC) our(PRP$) pilots(NNS) have(VBP) taken(VBN) it(PRP) and(CC) put(VB) it(PRP) into(IN) our(PRP$) manuals(NNS) .(.)