7: 1867
(1867, CD, 6)
[pobj]
Step 4: Dependency paths from key question phrases to the answer are computed:
Alaska⇒1867:
⇑pobj⇑prep⇑nsubj⇓prep⇓pobj
acquisition⇒1867:
⇑nsubj⇓prep⇓pobj
Step 5: The resulting pattern is stored:
Query:
When[1]+was[2]+NP[3]+VERB[4]
Path 3:
⇑pobj⇑prep⇑nsubj⇓prep⇓pobj
Path 4:
⇑nsubj⇓prep⇓pobj
Figure 1: The pattern creation algorithm exemplified in five key steps for the query “When was Alaska pur-chased?” and the answer sentence “The acquisition of Alaska happened in 1867.”
tify and align phrases. Word Alignment is im-
Note that one question can potentially match sev-
portant in many fields of NLP, e.g. Machine
eral patterns. The consequents contain descrip-
Translation (MT) where words in parallel, bilin-
tions of grammatical structures of potential an-
swer sentences that can be used to identify and
gual corpora need to be aligned, see (Och and
evaluate candidate sentences.
Ney, 2003) for a comparison of various statisti-
cal alignment models. In our case however we
4 Phrase Alignment
are dealing with a monolingual alignment prob-
lem which enables us to exploit clues not available
The goal of this processing step is to align phrases
for bilingual alignment: First of all, we can expect
from the question with corresponding phrases
many query words to be present in the answer sen-
from the answer sentences in the training data.
tence, either with the exact same surface appear-
Consider the following example:
ance or in some morphological variant. Secondly,
Query: “When was the Alaska territory pur-
there are tools available that tell us how semanti-
chased?”
cally related two words are, most notably Word-
Answer sentence: “The acquisition of what
Net (Miller et al., 1993). For these reasons we im-
would become the territory of Alaska took place
plemented a bespoke alignment strategy, tailored
in 1867.”
towards our problem description.
The mapping that has to be achieved is:
This method is described in detail in (Kaisser,
