Towards Events Annotated Corpus of Polish

Towards Events Annotated Corpus of Polish
Michał Marcińczuk, Marcin Oleksy, Jan Kocoń,
Tomasz Bernaś and Michał Wolski
{michal.marcinczuk, marcin.oleksy, jan.kocon,
tomasz.bernas, michal.wolski}@pwr.edu.pl
Institute of Informatics
Wrocław University of Technology
Wybrzeże Wyspiańskiego 27,
Wrocław, Poland
April 10, 2015
Work financed as part of the investment in the CLARIN-PL research infrastructure
funded by the Polish Ministry of Science and Higher Education.
Introduction » Event recognition
Event recognition
Part of Natural Language Engineering
Major task in Information Extraction (IE) field
The goal is to identify actions and some states described in
text:
M. Marcińczuk, et al.
Textual evidence of an event
Event arguments (who? when? where? ...)
Event attributes (specific/generic, true/false, past/present ...)
Structural representation of events ⇒ data mining
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Introduction » Example
Example (1/2)
Text
Two Russians and a Frenchman left the Mir and endured a
rough landing on the snow-covered plains of Central Asia on
Thursday. (...) The two Russians arrived on the Mir last
August (...). Solovyou celebrated his 50th birthday during
his six-month space voyage.
source: http://www.themoscowtimes.com/
What to annotate:
Temporal expressions
Events
Signals
Links
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Introduction » Example
Example (2/2)
Rob O’Neil - weteran NAVY Seals, który zastrzelił w maju
2011 roku Osamę bin Ladena, po 16 latach służby odszedł z
jednostki i ujawnił swoją tożsamość. Nazwisko mężczyzny
wyszło na jaw po tym, jak amerykańska stacja informacyjna
Fox News poinformowała, że żołnierz udzieli w niej wywiadu
i opowie o całej akcji wymierzonej w szefa Al-Kaidy. Jak
mówi jego ojciec, nie boją się zemsty ze strony Państwa
Islamskiego, ani innych organizacji terrorystycznych.
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Introduction » Example
Example (2/2) - timeline
maju 2011 roku
zastrzelił
odszedł
ujawnił
poinformowała
wyszło (na jaw)
mówi
udzieli
opowie
(nie) boimy się
akcji
zemsty
służby
16 lat
Rob O’Neil
Fox News
ojciec
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Events in TimeML » What to annotate?
What to annotate?
Tensed Verbs: A fresh flow of lava, gas and debris erupted there
Saturday.
Untensed verbs: Prime Minister Benjamin Netanyahu called the
prime minister of the Netherlands to thank him for
thousands of gas masks (...).
Nominalizations: Israel will ask the US to delay a military strike
against Iraq until the Jewish state is fully prepared for
a possible Iraqi attack.
Adjectives: A Philippine volcano, dormant for six centuries,
began exploding with searing gases, thick ash and
deadly debris.
Prepositional phrases: All 75 people on board the Aeroflot Airbus
died.
Predicative Clauses: "There is no reason why we would not be
prepared," Mordechai told the Yediot Ahronot daily.
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Events in TimeML » Classes
Classes
REPORTING: say, report, announce, ...
PERCEPTION: see, hear, watch, feel, ...
ASPECTUAL: begin, start, finish, stop, continue, ...
I_ACTION: attempt, try, promise, offer, regret, ...
I_STATE: believe, want, wish, ...
STATE: be on board, kidnapped, recovering, love, ...
OCCURRENCE: die, crash, build, merge, sell, take advantage of, ...
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Event annotation » Textual mentions
Textual mentions (1/2)
Step 1 — Annotation of textual mentions of events
Event X
Event Y
Action
dynamic
State
static
Reporting
Verb
We know that X
occured or not
Auxiliary
verbs
Perception
D
A
Ascpectual
I_Action
B
We do not know
if X occured or not
I_State
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Light_predicate
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C
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Event annotation » Textual mentions
Textual mentions (2/2)
Action
pracy
spotkania
spotkanie
zginęło
odbędzie
I_Action
zapowiedział
pozwala
zgodę
wymaga
proszę
53
43
40
35
35
13
10
9
9
8
State
ma
93
mają
47
mieć
23
miał
23
oznacza 21
I_State
można
163
może
120
ma
45
trzeba
42
należy
39
Reporting
powiedział
41
mówi
26
stwierdził
13
mówił
13
informuje
9
Light_predicate
doszło
5
ma
5
ulec
5
dokonał
4
prowadzić
3
Perception
widać
18
zobacz
18
zobaczyć
12
widzę
7
posłuchać 5
Aspectual
zakończył 13
zaczęła
12
zaczyna
10
rozpoczął 9
końcowa
8
Table 1: Top 5 mentions (ortographic forms) for each category.
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What has been done? » Event mentions
Event mentions in KPWr
Documents in KPWr
Documents annotated
Annotations (unique)
Annotations (total)
0
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2000
4000
6000
8000
10000
1634
558
9557
24023
12000
14000
Action
Aspectual
I_Action
I_State
Light_predicate
Perception
Reporting
State
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What has been done? » Annotation agreement
Annotation agreement
Positive specific agreement between two annotators (A and B) for
100 documents from KPWr.
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Events (only spans)
Events
Action
Aspectual
Perception
Reporting
I_Action
I_State
State
Light_predicate
3184
2561
2085
46
20
39
23
115
213
20
393
1016
766
4
2
29
19
61
92
41
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664
1287
418
10
37
28
21
70
634
20
85.76%
68.98%
77.89%
86.79%
50.63%
57.78%
53.49%
63.71%
36.98%
39.60%
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What is to be done? » Event arguments
Arguments
Step 2 — Linking event mentions with generic arguments
agency — who performed the action,
temporal — when the action was performed and how long the
action was being performed or how long the state was
present,
spatial — where the action was performed.
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What is to be done? » Event attributes
Attributes
Step 3 — described event attributes
generality — specific or general,
tense — past, present or future,
polarity — affirmative or negative,
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What is to be done? » Event linking
Event linking
Step 4 — event linking
subordination link — relations between events (modal, factive,
counter-factive, evidential, negative evidential,
conditional)
aspectual link — relation between an aspectual event and its
argument event.
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»
The End
M. Marcińczuk, et al.
Thank you for your attention.
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