NER at sentence level or document level?
Should NER models (LSTM or CRF) take input training data at sentence level or paragraph level?
Let's say we have this input text, and we would like to do Named Entity Extraction:
GOP Sen. <PERSON> was assaulted in his home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police. State troopers responded to a call to the senator\'s residence at 3:21 p.m. Friday. Police arrested a man named <PERSON>, who they allege "intentionally assaulted" <PERSON>, causing him "minor injury". <PERSON>, 59, of Bowling Green was charged with one count of fourth-degree assault. As of Saturday afternoon, he was being held in the Warren County Regional Jail on a $5,000 bond.
1. 458
Paragraph level: we can take it as one record and each token is marked by the entity label. Model has ONE record with LONG sequence.
2. Sentence level: we first intelligently split the paragraph into 5 correct sentences, and each token in each sentence is marked by the entity label. Model has FIVE records with shorter sequences:
0) GOP Sen. <PERSON> was assaulted in his home in Bowling Green, Kentucky, on Friday, according to Kentucky State Police.
1) State troopers responded to a call to the senator's residence at 3:21 p.m. Friday.
2) Police arrested a man named <PERSON>, who they allege "intentionally assaulted" <PERSON>, causing him "minor injury".
3) <PERSON>, 59, of Bowling Green was charged with one count of fourth-degree assault.
4) As of Saturday afternoon, he was being held in the Warren County Regional Jail on a $5,000 bond.
Which one gives the NER modeling a better NER performance?
I tend to think sentence level is better, however, shouldn't LSTM memory cells be trained to remember or forget states automatically if given long paragraphs? Especially when the sentence splitting can also potentially make mistakes, for instance:
1) State troopers responded to a call to the senator's residence at 3:21 p.m. Friday.
could have been
1) State troopers responded to a call to the senator's residence at 3:21 p.m.
2) Friday. 588