Skip to main content

Advertisement

Table 1 Rule set to extract causal relations from tweets

From: Extracting health-related causality from twitter messages using natural language processing

# Causal relation types Dependency rules Examples
1 A (noun) caused B {} = subj < subj ({+ Causal verb +} = target >dobj {} = cause) Stress causes insomnia
2 A (verb-ing) caused B {} = subj < csubj ({+ Causal verb +} = target >dobj {} = cause) Over thinking can increase anxiety and cause insomnia.
3 B was caused by A {} = ncsubjpass<nsubjpass({+ Causal verb +} = target >/nmod:agent/{} = cause) My insomnia was caused by stress.
4 A is a reason of B Causal noun + < nsubj ({} = target > /nmod:of/ {} = cause) Stress is a reason of my insomnia
5 B was caused by A (verb-ing) {} = nsubj< nsubjpass ({} = target >/advcl:by/ + Causal noun) Insomnia was caused by overthinking
6 A results “in/to/from” B Causal verb + < [nc] subj ({} = target> /nmod:(to|in|from)/{} = cause) Stress results to insomnia.