Information Technology Reference
In-Depth Information
...
<t lemma="Spannung-Steuerung" word="Spannungssteuerung" pos="NN"
id="sentences._108_28" />
<t lemma="in" word="im" pos="APPRART"
id="sentences._108_29" />
<t lemma="Wickel-Kopf-Bereich" word="Wickelkopfbereich" pos="NN"
id="sentences._108_30" />
<t lemma="hindeuten" word="hindeuten" pos="VVFIN" id="sentences._108_31" />
</terminals>
<nonterminals>
<nt id="sentences._108_500" cat="PP">
<edge idref="sentences._108_3" label="NK" />
<edge idref="sentences._108_2" label="DA" />
<edge idref="sentences._108_1" label="DA" />
</nt>
...
Fig. 4.11.
XML representation of a portion of the parse tree from Figure 4.10.
Phrase type
NN
Grammatical function
NK
Termi na l
(is the constituent a terminal or non-terminal node?)
1
Path
(path from the target verb to the constituent, denoting u(up) and d(down) for the direction)
uSdPPd
Grammatical path
(like Path, but instead of node labels, branch labels are considered)
uHDdMOdNK
Path length
(number of branches from target to constituent)
3
Partial path
(path to the lowest common ancestor between target and constituent)
uPPuS
Relative Position
(position of the constituent relative to the target)
left
Parent phrase type
(phrase type of the parent node of the constituent)
PP
Target
(lemma of the target word)
hindeuten
Target POS
(part-of-speech of the target)
VVFIN
Passive
(is the target verb passive or active?)
0
Preposition
(the preposition if the constituent is a PP)
none
Head Word
(for rules on head words refer to [5])
Spannung-Steuerung
Left sibling phrase type
ADJA
Left sibling lemma
kontinuierlich
Right sibling phrase type
none
Right sibling lemma
none
Firstword, Firstword POS, Lastword, Lastword POS
(in this case, the constituent has only one word,
thus, these features get the same values: Spannung-Steuerung and NN. For non-terminal constituents
like PP or NP, first word and last word will be different.)
Frame
(the frame evoked by the target verb)
Evidence
Role
(this is the class label that the classifier will learn to predict. It will be one of the roles related
to the frame or none, for an example refer to Figure 4.12.)
none
If a sentence has several clauses where each verb evokes a frame, the feature
vectors are calculated for each evoked frame separately and all the vectors participate
in the learning.
4.4.6 Annotation
To perform the manual annotation, we used the Salsa annotation tool (publicly
available) [11]. The Salsa annotation tool reads the XML representation of a parse
tree and displays it as shown in Figure 4.12. The user has the opportunity to add
frames and roles as well as to attach them to a desired target verb. In the example of
Figure 4.12 (the same sentence of Figure 4.10), the target verb
hindeuten
(point to)
evokes the frame Evidence, and three of its roles have been assigned to constituents of
the tree. Such an assignment can be easily performed using point-and-click. After this
Search WWH ::
Custom Search