Answering complex, list and context questions with LCC's Question-Answering Server Page: 2
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Processing of Complex Questions
Processing of List Questions
Quantification Scalar Processing of Context Questions
Figure 1: Architecture of LCC's QASTM
ferent kinds of questions were evaluated: (1) complex
questions, that expect an answer from the text col-
lections without knowing if such an answer exists; (2)
list questions, requiring a list of answers; and (3) con-
text questions, in which the question was considered
in the context of the previous questions and answers
processed by the system. Three distinct evaluations
were conducted, but a single question-answering archi-
tecture handled all three cases.
The Question Processing is different for each of the
three kinds of questions that were evaluated. For com-
plex, factual questions like "Q1147: What is the Statue
of Liberty made of?", the processing involves at first
the recognition of the expected answer type from an
off-line taxonomy of semantic types. In TREC-10, the
factual questions were far more complex than those
evaluated in TREC-9 and TREC-8 because frequently
the expected answer type could not be easily identi-
fied. For example, in the case of the question Q1147
virtually anything could be a criterion. To help narrow
down the search for the expected answer type and to
generate robust processing at the same time, a set of
bridging inference procedures were encoded. For ex-
ample, in the case of the question Q1147, the bridging
inference between the question and the expected an-
swer type encode several meronymy relations between
different materials and the Statue of Liberty. Instead
of searching for the expected answer type in each re-
trieved paragraph LCC's QASTM looks for meronymy
relations involving any of the keywords used in the
For questions expecting a list of answers, the quan-
tification scalar, defining the size of the list, is iden-
tified at the time of question processing and used
when the answers are extracted and fused together.
For example, in the case of question "Name 15 reli-
gious cults." the expected answer type is ORGANIZA-
TION of the type religious cult and the quantifier is 15.
Sometimes, the expected answer type have multiple at-
tributes, e.g. "Name 4 people from Massachusetts who
were candidates for vice-president." Such attributes
are translated into keywords that retrieve the relevant
document passages or paragraphs.
If the question needs to be processed in the context
of the previous questions and answers, a coreference
resolution process takes place prior to the recognition
of the expected answer type. For example, the pro-
noun this from the question "On what day did this
happen?" is resolved as the event mentioned in its
preceding question, i.e. "Which museum in Florence
was damaged by a major bomb explosion in 1993?".
The reference resolution entails the usage of the key-
words defining the antecedent along with the keywords
extracted from the current question.
The Document Processing module uses a paragraph
index to retrieve document passages that (a) contain
the keywords from the query, and (b) contain either a
concept of the expected answer type or a relation indi-
cated by the bridging inference mechanisms. However,
if insufficient evidence of the paragraph relevance ex-
ists, pragmatic information is passed back to the feed-
back loop that reformulates the query searching for the
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Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Surdeanu, Mihai; Mihalcea, Rada, 1974-; Gîrju, Corina R. et al. Answering complex, list and context questions with LCC's Question-Answering Server, paper, November 2001; [Gaithersburg, Maryland]. (digital.library.unt.edu/ark:/67531/metadc83297/m1/2/: accessed May 27, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.