This repo is for task 2 of relation extraction problem

literatury review of the RE (done)

notes for concept and coding

main entry of code

python main.py

require stanfordcorenlp

1. PDF files from Mendeley database
2. Sentence splitting
3. Tokenizing
4. POS
5. Name entity recognition
6. Parsing
7. Relation extraction So far, step 1 & 2 are finished by previous project. We apply the stanfordcorenlp api to do step 3-6, and focus on the RE. We are using rule-based approaches and manually define the rules. we save all sentences from one PDF file in the database for test, namely 'sen_pdf1.txt' in the repo. Parsing results are transfered as input of step 7 after step 1~6

a relation is said to be negated if no node in the candidate relation contains Number.

• effector of the relation: the name entity appearing first in the extracted relation, i.e. with the smaller sentence position
• The roles are switched if some form of passive construct is detected

Noun phrase chunks connected to each other by a and, or, nn, det, or dep dependency form an enumeration. If a noun phrase chunk contains more than one protein name, these are considered to describe alternative agents/targets.

The words contained in candidate relations are checked against a set of relation restriction terms.

• focus domain corpora will be generated by:
  • scanning our database and checking noun frequency
  • and check with public corpora in this field
  • we can add a filter in NER

• Negation check
• find triple {NN VB CD} in a relation

1. need more rules
2. corpora
3. try dependency path
4. train the parser?