from the WSJ training database:
      GTS (takeover)
      GTS (merge)
      GTS (buy -out)
      GTS (acquire)
with

=0.00145576
=0.00094518
=0.00272580
=0.00057906

      SIM (takeover,merge)   = 0.190444
      SIM (takeover,buy -out) =0.157410
      SIM (takeover,acquire) =0.139497
      SIM (merge, buy -out)  = 0.133800
      SIM (merge,acquire)    =0.263772
      SIM (buy -out,acquire) = 0.109106
Therefore both takeover and buy-out can be used to spe-
cialize merge or acquire. With this filter, the relation-
ships between takeover and buy-out and between merge
and acquire are either both discarded or accepted as
synonymous. At this time we are unable to tell
synonymous or near synonymous relationships from
those which are priinarily complementary, e.g., man and
woman.
    In ThEC-1 the impact of query expansion through
term similarities on the system's overall performance
was generally disappointing. For TREC-2 we have made
a number of changes to the term cottelation model, but
again time limitations prevented us from properly testing
all options. Among the most important changes are:
(1)  Exclusion of pairs obtained from SUBJEGF--
     VERB relations: we detennined that these con-
     texts are generally of litfie use as neither subject
     nor verb subeategorizes well for the other. More-
     over we observed that the presence of these pairs
     was the source of many unwanted term associa-
     tions.11
(2)  Automatic pruning of low~ontent terms from the
     queries: terms with low idf weights, terms with
     low information contribution weights that are
     elements of compound terms, are removed from
     queries before database search. As we tuned
     various cutoff thresholds we noted that a
     significant increase in both recall and precision
     could be obtained. 12


     Subject-Verb pairs were retained as eompound terms, however.
    12 The Information Contribution Ineasure indicates the strength of

                                                  j;j
word pairings, and is defined as IC (x, fx,y]) =     where f,~ is
                                                 n,+d~-l
the absolute frequency of pair [x,y] in the corpus, n, is the frequency of
term x at the head position, and d~ is a dispersion parameter understood
as the number of distinct Syntactic contexts in which term x is found.


                                                          129

            word               cluster

       takeover       merge, buy-out, acquire, bid

       benefit        compensate, aid, expense

       capital        cash, jund, money

       staff          personnel, employee, force

       attract        lure, draw, woo

       sensitive      crucial, difficult, critical

       speculate      rumor, uncertainty, tension

       president      director, executive, chairman

       vice           deputy

       outlook        forecast, prospect, trend

       law            rule, policy, legislate, bill

       earnings       profit, revenue, income

       portfolio      asset, invest, loan

       inflate        growth. demand, earnings

       industry       business, company, market

       growth         increase, rise, gain

       firm           bank, concern, group, unit

       environ        climate, condition, situation

       debt           loan, secure, bond

       lawyer         attorney

       counsel        attorney, administrator, secretary

       compute        machine, sofiware, equipment

       competitor     rival, competition, buyer

       alliance       partnership, venture, consortium

       big            large, major, huge, significant

       fight          battle, attack, war, challenge

       base           facile, source, reserve, support

       shareholder    creditor, customer, client
                      investor, stockholder


Table 1. Selected clusters obtained from syntactic contexts, derived
from approx. 40 million words of WSJ text, with weighted Tanimoto
formula.