=Paper=
{{Paper
|id=Vol-1172/CLEF2006wn-QACLEF-SacaleanuEt2006
|storemode=property
|title=DFKI-LT at the CLEF 2006 Multiple Language Question Answering Track
|pdfUrl=https://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-SacaleanuEt2006.pdf
|volume=Vol-1172
|dblpUrl=https://dblp.org/rec/conf/clef/SacaleanuN06a
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==DFKI-LT at the CLEF 2006 Multiple Language Question Answering Track==
https://ceur-ws.org/Vol-1172/CLEF2006wn-QACLEF-SacaleanuEt2006.pdf
DFKI-LT at the CLEF 2006
Multiple Language Question Answering Track
Bogdan Sacaleanu and Günter Neumann
LT-Lab, DFKI, Saarbrücken, Germany
{bogdan,neumann}@dfki.de
Abstract
The paper describes QUANTICO, a cross-language open domain question answering system for German
and English. The main features of the system are: use of preemptive off-line document annotation with
syntactic information like chunk structures, apposition constructions and abbreviation-extension pairs for
the passage retrieval; use of online translation services, language models and alignment methods for the
cross-language scenarios; use of redundancy as an indicator of good answer candidates; selection of the
best answers based on distance metrics defined over graph representations. Based on the question type two
different strategies of answer extraction are triggered: for factoid questions answers are extracted from best
IR-matched passages and selected by their redundancy and distance to the question keywords; for
definition questions answers are considered to be the most redundant normalized linguistic structures with
explanatory role (i.e., appositions, abbreviation’s extensions). The results of evaluating the system’s
performance by CLEF were as follows: for the best German-German run we achieved an overall accuracy
(ACC) of 42.33% and a mean reciprocal rank (MRR) of 0.45; for the best English-German run 32.98%
(ACC) and 0.35 (MRR); for the German-English run 17.89% (ACC) and 0.17 (MRR).
Categories and Subject Headings
H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information
Search and Retrieval; H.3.4 Systems and Software; I.7 [Document and Text Processing]: I.7.1 Document
and Text Editing; I.7.2 Document Preparation; I.2 [Artificial Intelligence]: I.2.7 Natural Language
Processing
General Terms
Algorithms, Design, Experimentation
Keywords
Open Domain Question Answering, Monolingual German, Cross-Language German/English
1. Introduction
QUANTICO is a cross-language open domain question answering system developed for both English and
German open-domain question answering, cf. [2], [3]. It uses a common framework for both monolingual
and cross-language scenarios, with different workflow settings for each task and different configurations
for each type of question. For tasks with different languages on each end of the information flow (question
and documents) we cross the language barrier rather on the question than on the document side by using
free online translation services, linguistic knowledge and alignment methods. An important aspect of
QUANTICO is the triggering of specific answering strategies by means of control information that has
been determined by the question analysis tool, e.g., question type and expected answer type, see [3] for
more details. Through the offline annotation of the document collection with several layers of linguistic
information (chunks, appositions, named entities, sentence boundaries) and their use in the retrieval
process, more accurate and reliable information units are being considered for answer extraction, which is
based on the assumption that redundancy is a good indicator of information suitability. The answer
selection component normalizes and represents the context of an answer candidate as a graph and computes
its appropriateness in terms of the distance between the answer and question keywords.
We will begin giving a short overview of the system and presenting its working for both factoid and
definition questions in monolingual and cross-language scenarios. We will then continue with a short
description of each component and close the paper with the presentation of the CLEF evaluation results.
2. System Overview
QUANTICO uses a common framework for both monolingual and cross-language scenarios, but with
different configurations for each type of question (definition or factoid) and different workflow settings for
each task (DE2DE, DE2EN or EN2DE).
Concerning the workflow settings, the following things are to be mentioned. For the monolingual
scenario (DE2DE) the workflow is as follows (according to the architecture in the Figure 1): 1-4-5-6/7 with
the last selection depending on the question type. For a cross-language scenario, the workflow depends on
the language of the question: for German questions and English documents (DE2EN) the workflow is 1-2-
3-4-5-6/7, that is, the question is first analyzed, then translated and aligned to its translations, so that based
on the generated QAObj and the alignments a new English QAObj is being computed; for English questions
and German documents (EN2DE) the workflow is 2-1-4-5-6/7, that is, the question is first translated and
then the best translation – determined according to linguistic completeness – is being analyzed resulting in
a QAObj. The difference in the system’s workflow for the cross-language scenario comes with our choice
of analyzing only German questions, since our analysis component, based on the SMES parser [1], is very
robust and accurate. In the presence of a Question Analysis component with similar properties for English
questions, the workflow would be the same (1-2-3-4-5-6/7) independent of the question’s language.
Regarding the component configurations for each type of question (temporal, definition or factoid) the
difference is to be noted only in the Passage Retrieval and Answer Extraction components. While the
Retrieve process for the factoid questions builds on classic Information Retrieval methods, for definition
questions is merely a look-up procedure in a repository of offline extracted syntactic structures as
appositions, chunks and abbreviation-extension pairs. For the Answer Extraction component the distinction
consists in different methods of computing the clusters of candidate answers: for factoid question, where
the candidates are usually named entities or chunks, is based on coreference (John ~ John Doe) and stop-
word removal (of death ~ death), while for definition questions, where candidates can vary from chunks to
whole sentences, is based on topic similarity (Italian designer ~ the designer of a new clothes collection).
3. Component Description
Following is a description of QUANTICO’s individual components along with some examples.
3.1. Question Analysis
In context of a QA system or information search in general, we interpret the result of a NL question
analysis as a declarative description of search strategy and control information, see [3]. Consider, for
example, the NL question result in form of XML for the question “In welcher Stadt fanden 2002 die
olympischen Winterspile statt?” (The Olympic winter games took place 2002 in which town?), where the
value of tag a-type represents the expected answer type, q-type the answer control strategy, and q-focus and
q-scope additional constraints for the search space:
In welcher Stadt fanden 2002 die olympischen Winterspiele statt?Stadtstattfind_winter#spielC-COMPLETIONLOCATIONfandenStadt2002olympischenWinterspiele2002
Parts of the information can already be determined on basis of local lexico-syntactic criteria (e.g., for the
Wh-phrase where we can simply infer that the expected answer type is location). However, in most cases
we have to consider larger syntactic units in combination with information extracted from external
knowledge sources. For example for a definition question like “What is a battery?” we have to combine
syntactic and type information from the verb and the relevant NP (e.g., combine definite/indefinite NPs
together with certain auxiliary verb forms) in order to distinguish it from a description question like “What
is the name of the German Chancellor?” In our QAS, we are doing this by following a two-step parsing
schema:
o in a first step a full syntactic analysis is performed using the robust parser SMES (cf.[1]) and
o in a second step a question-specific semantic analysis.
During the second step, the values for the question tags a-type, scope and s-ctr are determined on basis of
syntactic constraints applied on the dependency analysis of relevant NP and VP phrases (e.g., considering
agreement and functional roles), and by taking into account information from two small knowledge bases.
They basically perform a mapping from linguistic entities to values of the questions tags, e.g., trigger
phrases like name_of, type_of, abbreviation_of or a mapping from lexical elements to expected answer
types, like town, person, and president. For German, we additionally perform a soft retrieval match to the
knowledge bases taking into account on-line compound analysis and string-similarity tests. For example,
assuming the lexical mapping Stadt → LOCATION for the lexeme town, then automatically we will also
map the nominal compounds Hauptstadt (capital) and Großstadt (large city) to LOCATION.
A main aspect in the adaptation and extension of the question analysis component for the Clef-2006 task
concerned the recognition of the question type, i.e., simple factoid and list factoid questions, definition
questions and the different types of the temporally restricted questions. Because of its high degree of
modularity of the question analysis component, the extension only concerns the semantic analysis sub-
component. Here, additional syntactic-semantic mapping constraints have been implemented that enriched
the coverage of the question grammar, where we used the question set of the previous Clef campaigns as
our development set.
3.2. Translation Services and Alignment
We are using two different methods for responding questions asked in a language different from the one of
the answer-bearing documents. Both employ online translation services (Altavista, FreeTranslation, etc.)
for crossing the language barrier, but at different processing steps: before and after formalizing the user
information need into a QAObj.
The a priori–method translates the question string in an earlier step, resulting in several automatic
translated strings, of which the best one is analyzed by the Question Analysis component and passed on to
the Passage Retrieval component. This is the strategy we use in an English–German cross-lingual setting.
To be more precise: the English source question is translated into several alternative German questions
using online MT services. Each German question is then parsed with SMES, our German parser. The
resulting query object is then weighted according to its linguistic well–formedness and its completeness
with respect to the query information (question type, question focus, answer–type).
The assumption behind this weighting scheme is that “a translated string is of greater utility for subsequent
processes than another one, if its linguistic analysis is more complete or appropriate.”
The a posteriori–method translates the formalized result of the Query Analysis component by
using the question translations, a language modeling tool and a word alignment tool for creating a mapping
of the formal information need from the source language into the target language. We illustrate this strategy
in a German–English setting along two lines (using the following German question as example: “In
welchem Jahrzehnt investierten japanische Autohersteller sehr stark?”):
• translations as returned by the on-line MT systems are being ranked according to a language
model
o In which decade did Japanese automakers invest very strongly? (0.7)
o In which decade did Japanese car manufacturers invest very strongly? (0.8)
• translations with a satisfactory degree of resemblance to a natural language utterance (i.e.
linguistically well-formedness), given by a threshold on the language model ranking, are aligned based
on several filters: dictionary filter - based on MRD (machine readable dictionaries), PoS filter - based
on statistical part-of-speech taggers, and cognates filter - based on string similarity measures (dice
coefficient and LCSR (lowest common substring ratio)).
In: [in:1.0] 1.0
welchem: [which:0.5] 0.5
Jahrzehnt: [decade:1.0] 1.0
investierten: [invest:1.0] 1.0
japanische: [Japanese:0.5] 0.5
Autohersteller: [car manufacturers:0.8, auto makers:0.1] 0.8
sehr: [very:1.0] 1.0
stark: [strongly:0.5] 0.5
3.3. Passage Retrieval
The preemptive offline document annotation refers to the process of annotating the document collections
with information that might be valuable during the retrieval process by increasing the accuracy of the hit
list. Since for factoid questions the expected answer type is usually a named entity type, annotating the
documents with named entities provides for an additional indexation unit that might help to scale down the
range of retrieved passages to those only containing the searched answer type. The same practice applies
for definition questions given the known fact that some structural linguistic patterns (appositions,
abbreviation-extension pairs) are used with explanatory and descriptive purpose. Extracting these kind of
patterns in advance and looking up the definition term among them might return more accurate results than
those of a search engine.
The Generate Query process mediates between the question analysis result QAObj (answer type, focus,
keywords) and the search engine (factoid questions) or the repository of syntactic structures (definition
questions) serving the retrieval component with information units (passages). The Generate Query process
builds on an abstract description of the processing method for every type of question to accordingly
generate the IRQuery to make use of the advanced indexation units. For example given the question “What
is the capital of Germany?”, since named entities were annotated during the offline annotation and used as
indexing units, the Query Generator adapts the IRQuery so as to restrict the search only to those passages
having at least two locations: one as the possible answer (Berlin) and the other as the question’s keyword
(Germany).
It is often the case that the question has a semantic similarity with the passages containing the answer,
but no lexical overlap. For example, for a question like “Who is the French prime-minister?”, passages
containing “prime-minister X of France”, “prime-minister X … the Frenchman” and “the French leader of
the government” might be relevant for extracting the right answer. The Extend process accounts for
bridging this gap at the lexical level, either through look-up of unambiguous resources or as a side-effect of
the translation and alignment process (see [4]).
Whereas the Retrieve process for definition questions is straightforward for cases when the offline
annotation repository lookup was successful, in other cases it implies an online search of the document
collection and retrieval of only those passages that might bear a resemblance to a definition. The selection
of these passages is attained by matching them against a lexico-syntactic pattern of the form:
.+
whereby is being defined as a closed list of verbs like “is”, “means”, ”signify”, “stand
for” and so on.
3.4. Answer Extraction
The Answer Extraction component is based on the assumption that the redundancy of information is a good
indicator for its suitability. The different configurations of this component for factoid and definition
questions reflect the distinction of the answers being extracted for these two question types: simple chunks
(i.e., named entities and basic noun phrases) and complex structures (from phrases through sentences) and
their normalization. For factoid questions having named entities as expected answer type the Group
(normalization) process consists in resolving cases of coreference, while for definition questions with
phrases and sentences as possible answers more advanced methods are being involved. The current
procedure for clustering definitions consists in finding out the focus of the explanatory sentence or the head
of the considered phrase. Each cluster gets a weight assigned based solely on its size (definition questions)
or using additional information like the average of the IR-scores and the document distribution for each of
its members (factoid questions).
3.5. Answer Selection
Using the most representative sample (centroid) of the answer candidates’ clusters, the Answer Selection
component sorts out a list of top answers based on a distance metric defined over graph representations of
the answer’s context. The context is first normalized by removing all functional words and then represented
as a graph structure. The score of an answer is defined in terms of its distance to the question concepts
occurring in its context and the distance among these.
4. Evaluation Results
We participated in three tasks: DE2DE, EN2DE and DE2EN, where the summary of the results can be
found in the table below.
Task Overall accuracy Factoid Definition Temporal NIL (correct\returned)
dfki061dede 80/189=42.33% 59/156=37.82% 21/33=63.64% 0 9\32
dfki062dede 63/189=33.33% 47/156=30.13% 16/33=48.48% 0 8\29
dfki061ende 62/188=32.98% 44/156=28.21% 18/32=56.25% 0 12\57
dfki062ende 50/188=26.60% 34/156=21.79% 16/32=50.00% 0 13\58
dfki061deen 34/190=17.89% 26/150=17.33% 8/40=20.00% 0 8\65
For the tasks DE2DE and EN2DE we submitted two runs, for DE2EN only one. Compared to the results
from last year, we were able to keep our performance for the monolingual German task (2005: 43.50%).
For the task English to German we were able to improve our result (2005: 25.50%). But for the task
German to English we observed a decrease (2005: 23.50%). Furthermore, although the question analysis
component was able to identify the different types of temporal questions, in no cases we were able to
correctly identify and extract answers for those questions. It is still unclear, why.
References
[1] G. Neumann and J. Piskorski. A shallow text processing core engine. Computational Intelligence,
18(3):451–476, 2002.
[2] G. Neumann and B. Sacaleanu. Experiments on Robust NL Question Interpretation and Multi-
layered Document Annotation for a Cross-Language Question/Answering System. In C. Peters et
al. (Eds): Clef 2004, LNCS 3491, pp. 411-422, Springer Berlin Heidelberg, 2005.
[3] G. Neumann and B. Sacaleanu. Experiments on Cross-Linguality and Question-Type Driven
Strategy Selection for Open-Domain QA. In C. Peters et al. (Eds): Clef 2005, LNCS 4022, pp.
429-438, Springer Berlin Heidelberg, 2006.
[4] B. Sacaleanu and G. Neumann: Cross-Cutting Aspects of Cross-Language Question Answering
Systems. In Proceedings of the EACL workshop on Multilingual Question Answering - MLQA'06,
Trento, Italy, 2006.