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id_794F_2
Ratio of Tags per Word for Sentence no: 4
Word Ambiguity Ratio: 1.57143
Lookahead Start Active Chart
0-[av-There, v-There] 1-[v- (is)] 2-[d-no, i-no, n-no] 3-[n-trade_gap] 4-[av-between, p-between] 5-[n-China_and_US] 6-[x- .] 


New Ratio of Tags per Word after FSA Disambiguation: 
1.28571
0-[v-There] 1-[v- (is)] 2-[d-no, i-no, n-no] 3-[n-trade_gap] 4-[p-between] 5-[n-China_and_US] 6-[x- .] 

0-[v-There] 1-[v- (is)] 2-[d-no, i-no, n-no] 3-[n-trade_gap] 4-[p-between] 5-[n-China_and_US] 6-[x- .] 


PennTreebank Style Tagging

4
There-vbd
(is)-vbz
no-rb
trade_gap-nn
between-in
China_and_US-nns
. - punto

Tagging and Syntactic Constituency

[There-vin, (is)-ause, no-neg, trade_gap-n, between-p, China_and_US-n, . - punto]

cp-[ibar-[There-vin-ibar], ibar-[ (is)-ause-ibar], savv-[no-neg-savv], sn-[trade_gap-n-sn], sp-[between-p-sp, China_and_US-n-sn], cp-[. - punto-cp]] 

4


Functional and Syntactic Constituency

ibar-[There-vin-ibar]
savv-[no-neg-savv]
ibar-[ (is)-ause-ibar]
xcomp-[trade_gap-n-sn]
obl-[between-p-sp, China_and_US-n-sn]

Output of Anaphora Resolution Module

all_prons([1-[]])
main  resolved as  China  with semantic index  id3
second  resolved as  trade_gap  with semantic index  id5

############   TOPICS   ############
MAIN TOPIC:  'China'
SECONDARY TOPIC:  trade_gap


Augmented Head Dependent Indexed Structures

'ncmod-modal'(between, 'There'-cl3, 'China_and_US'-sn3).
'xcomp-prop'('There'-cl3, trade_gap-sn2).
'subj-agent'('There'-cl3, you-sn1).
tense(pres, '_', be).
voice(active, '_', be).
voice(active, '_', 'There').

Semantic Interpretation and Informational Structure

[5, indirect, There, nil, active, nil, nil, nil, indic, nil, attivita]
sinfo(5, main/prop, external, factive, null, background, attivita, There, nil, explanation, objective, nil)


id_794F_2
Discourse Relations and Discourse Structure

sd_structure(id_794F_2, 5, objective, explanation, nil, There, background, level, down(3-5))
p_o_view:narrator

