▁did ▁exist , ▁then ▁deep ▁approaches ▁would ▁be ▁much ▁more ▁accurate ▁than ▁the ▁shall ow ▁approaches . ▁ ▁Also , ▁there ▁is ▁a ▁long ▁tradition ▁in ▁computational ▁lingu istics , ▁of ▁trying ▁such ▁approaches ▁in ▁terms ▁of ▁c oded ▁knowledge ▁and ▁in ▁some ▁cases , ▁it ▁is ▁hard ▁to ▁say ▁clearly ▁whether ▁the ▁knowledge ▁involved ▁is ▁lingu istic ▁or ▁world ▁knowledge . ▁The ▁first ▁attempt ▁was ▁that ▁by ▁Margaret ▁Master man ▁and ▁her ▁colle agues , ▁at ▁the ▁Cambridge ▁Language ▁Research ▁Unit ▁in ▁England , ▁in ▁the ▁ 1 9 5 0 s . ▁This ▁attempt ▁used ▁as ▁data ▁a ▁p unch ed - card ▁version ▁of ▁Rog et ' s ▁Th es aur us ▁and ▁its ▁number ed ▁" head s ", ▁as ▁an ▁indicator ▁of ▁topics ▁and ▁looked ▁for ▁repet itions ▁in ▁text , ▁using ▁a ▁set ▁intersection ▁algorithm . ▁It ▁was ▁not ▁very ▁successful , ▁but ▁had ▁strong ▁relationships ▁to ▁later ▁work , ▁especially ▁Y ar ow sky ' s ▁machine ▁learning ▁optim isation ▁of ▁a ▁th es aur us ▁method ▁in ▁the ▁ 1 9 9 0 s . ▁ ▁Sh allow ▁approaches ▁don ' t ▁try ▁to ▁understand ▁the ▁text . ▁They ▁just ▁consider ▁the ▁surrounding ▁words , ▁using ▁information ▁such ▁as ▁" if ▁bass ▁has ▁words ▁sea ▁or ▁fish ing ▁nearby , ▁it ▁probably ▁is ▁in ▁the ▁fish ▁sense ; ▁if ▁bass ▁has ▁the ▁words ▁music ▁or ▁song ▁nearby , ▁it ▁is ▁probably ▁in ▁the ▁music ▁sense ." ▁These ▁rules ▁can ▁be ▁automatically ▁derived ▁by ▁the ▁computer