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id_298T_2
Ratio of Tags per Word for Sentence no: 8
Word Ambiguity Ratio: 1.5
Lookahead Start Active Chart
0-[n-Tropical] 1-[n-storms, v-storms] 2-[n-cause, v-cause] 3-[a-severe] 4-[n-damage, v-damage] 5-[x- .] 


New Ratio of Tags per Word after FSA Disambiguation: 
1
0-[a-Tropical] 1-[n-storms] 2-[v-cause] 3-[a-severe] 4-[n-damage] 5-[x- .] 

0-[a-Tropical] 1-[n-storms] 2-[v-cause] 3-[a-severe] 4-[n-damage] 5-[x- .] 


PennTreebank Style Tagging

8
Tropical-jj
storms-nns
cause-vbd_vbn
severe-jj
damage-nns
. - punto

Tagging and Syntactic Constituency

[Tropical-ag, storms-n, cause-vt, severe-ag, damage-n, . - punto]

f-[subj-[Tropical-ag-sn, storms-n-sn], ibar-[cause-vt-ibar], sn-[severe-ag-sn, damage-n-sn], cp-[. - punto-cp]] 

8


Functional and Syntactic Constituency

subj-[Tropical-ag-sn, storms-n-sn]
ibar-[cause-vt-ibar]
obj-[severe-ag-sn, damage-n-sn]

Output of Anaphora Resolution Module

all_prons([1-[]])
main  resolved as  storm  with semantic index  id4
second  resolved as  damage  with semantic index  id5

############   TOPICS   ############
MAIN TOPIC:  storm
SECONDARY TOPIC:  damage


Augmented Head Dependent Indexed Structures

mod(sn2, damage, severe).
mod(sn1, storms, 'Tropical').
'obj-tema_eff'(cause-cl7, damage-sn2).
'subj-causer'(cause-cl7, storms-sn1).
tense(pres, '_', cause).
voice(active, '_', cause).

Semantic Interpretation and Informational Structure

[9, indirect, cause, pres, active, nil, nil, nil, indic, nil, attivita]
sinfo(9, main/prop, external, factive, null, background, attivita, cause, pres, narration, objective, nil)


id_298T_2
Discourse Relations and Discourse Structure

sd_structure(id_298T_2, 9, objective, narration, pres, cause, background, level, down(7-9))
p_o_view:narrator

