AUTOMATED QUESTION-ANSWERING TECHNIQUES

AND THE MEDICAL DOMAIN

Andrea Andrenucci

Department of Computer and System Sciences, Stockholm University/ Royal Institute of Technology

Forum 100, SE-16440, Kista, Sweden

Keywords: Automated Question Answering, Natural Language Interfaces, Medical Applications.

Abstract: The question-answering (QA) paradigm, i.e. the process of retrieving precise answers to natural language

(NL) questions, was introduced in late 1960-ies and early 1970-ies within the framework of Artificial

Intelligence. The advent of WWW and the need to provide advanced, user-friendly search tools has

extended the QA paradigm to a larger audience of people and a larger number of fields, including medicine.

This paper reviews and compares three main question-answering approaches based on Natural Language

Processing, Information Retrieval, and question templates, eliciting their differences and the context of

application that best suits each of them within the medical domain.

1 INTRODUCTION

The question-answering (QA) paradigm, i.e. the

process of retrieving precise answers to natural

language (NL) questions, was introduced in late

1960-ies and early 1970-ies within the framework of

Artificial Intelligence. From the beginning it was

mainly an academic research field and there were

hardly any commercially applicable QA

applications. The advent of WWW and the need to

provide advanced, user-friendly search tools has

extended the QA paradigm to a larger audience of

people and a larger number of fields, including

medicine, since medical content is one of the most

retrieved types of information on the WWW.

This paper discusses which of three major QA

approaches, i.e. deep Natural Language Processing

(NLP), Information Retrieval (IR) enhanced by

shallow NLP, and Template-based QA, better fit

medical applications, eliciting their context of

pertinence. To our knowledge, this is the first formal

comparison of the three QA approaches that focuses

on the medical domain.

The next three sections discuss the approaches

and provide some examples of their application in

the medical domain; section five and six pinpoints

the application areas that fit each technique.

2 NATURAL LANGUAGE

PROCESSING (NLP)

A common feature of deep NLP systems is that they

convert text input into formal representation of

meaning such as logic (first order predicate

calculus), semantic networks, conceptual

dependency diagrams, or frame-based

representations (Jurafsky and Martin, 2000, p. 502).

In other words deep NLP systems perform a

semantic analysis of text in NL. Semantic analysis is

the process of studying the meaning of a linguistic

input and giving a formal representation of it.

Jurafsky and Martin (2000, p. 548) provide a

possible approach for semantic analysis (see figure 1

on the next page): the user input is first passed

through a syntactic parser, whose output,

represented with a parse tree, is then processed by a

semantic analyzer which delivers a meaning

representation.

A medical QA system that implements this

approach is the ExtrAns system (Rinaldi et al.,

2004). The system derives logical representations of

both user questions and the documents in the

collection. The documents are analysed in an off-

line stage and their semantic form is stored in a

Database. In an on-line stage user questions are

converted into their semantic representation, prior to

being compared to the representations of the

documents in the matching process. When a match

207

Andrenucci A. (2008).

AUTOMATED QUESTION-ANSWERING TECHNIQUES AND THE MEDICAL DOMAIN.

In Proceedings of the First International Conference on Health Informatics, pages 207-212

 SciTePress

occurs, the sentences that originated the match are

extracted as possible answers to the user question.

Figure 1: The steps in Semantic Analysis.

Drawbacks of the deep NLP approach are its

computational intensiveness and its high processing

time (Andrenucci and Sneiders, 2005, Rinaldi et al.,

2004) as well portability difficulties (Andrenucci

and Sneiders, 2005, Hartrumpf 2006). Figure 2

(Androutsopoulos, Ritchie, and Thanisch, 1995)

shows the possible architecture of a typical deep

NLP QA system. Six components (linguistic front-

end) change when the input language changes, and

three components (domain-dependent knowledge)

change when the knowledge domain changes. The

domain dependent knowledge contains information

specific for the domain of interest: a lexicon and a

world model. The lexicon contains admissible

vocabulary words from the knowledge domain. The

world model describes the structure of the domain of

interest, i.e. the hierarchy of classes of the domain

objects, plus the properties and the constraints that

characterize the relationship between them. The

linguistic front-end parses and analyses the user

input in NL.

syntax rules

semantic

rules

lexicon

world model

mapping to

DB info

parser

semantic

interpreter

parse tree

DB query

generator

DB management

sys tem

res ponse

generator

NL input

res ponse

logical query

DB query

retrieved results

linguistic

front-end

domain-

dependent

knowledge

syntax rules

semantic

rules

lexicon

world model

mapping to

DB info

parser

semantic

interpreter

parse tree

DB query

generator

DB management

sys tem

res ponse

generator

NL input

res ponse

logical query

DB query

retrieved results

linguistic

front-end

domain-

dependent

knowledge

Figure 2: Architecture of a typical deep NLP system,

originally from Androutsopoulos et al., 1995.

3 INFORMATION RETRIEVAL

(IR) AND SHALLOW NLP

IR has evolved from document retrieval systems to

passage retrieval systems, which focus on retrieving

text passages rather than entire documents. Answers

are extracted with the help of shallow NLP, which

does not imply text understanding, i.e. semantic

analysis of NL input. Instead it focuses on extracting

text chunks, matching patterns or entities that

contain the answer to user questions. For instance in

a question like “Who discovered the polio

vaccine?”, the presence of the interrogative pronoun

“who” implies the extraction of an entity of type

“person name” associated with the keywords,

“discovered”, ”polio”, ”vaccine”.

This approach has been implemented in several

biomedical systems. Rindflesch et al. (2000) utilized

named entity recognition techniques to identify

drugs and genes in biomedical documents, then the

keywords which connect them (predicates). Craven

and Kumlien (1999) utilized “bag of words” at the

sentence level, to extract relations between proteins

and drugs from the information stored in Medline

articles (MEDLINE, 2006).

The IR approach is more domain-independent

than traditional NLP, but requires the answer to be

explicitly present in the text (Voorhees, 2001).

Furthermore answers retrieved with IR techniques

are less justified by the context, since they only

focus on extracting text snippets containing words

that are present in the user question (Laurent,

Seguela and Negre, 2006).

This approach is typical for information

extraction and is largely used in the Text REtrieval

Conferences (Voorhees, 2001), which aim at

comparing QA systems that retrieve mainly factoid

questions. Several systems that implement the

shallow NLP approach exploit data redundancy

(Brill et al., 2001), i.e. a number of text passages that

contain similar statements, in order to find reliable

answers. For example Sekimizu, Park and Tsujii

(1998) exploits domain specific verbs, which occur

frequently in MEDLINE abstracts, in order to locate

the biomedical terms that are respectively subject

and object terms for the verbs, and thereafter classify

their relations (e.g. Protein X regulates Protein Y).

Similarly Spasic, Nenadic, and Ananiadou (2003)

measure frequent co-occurrences of biomedical

verbs with unclassified terms in order to extract

domain specific terms.

A medical search system that implements both

IR techniques and deeper NLP techniques is

PERSIVAL (McKeown et al., 2001). The system

Input

Parse

Tree

Syntactic

Parser

Output

Semantic

Representation

Semantic

Analyser

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supports user search and summarization of medical

information with the help of representations of

medical texts and patient records. The system

processes medical documents with part of speech

tools and with a finite state grammar (that regulates

syntactic constraints) in order to extract multi-word

terms. This step is similar to the syntactic analysis

provided with the help of syntax rules in fig. 2.

Also similarly to the deep NLP approach, this

system utilizes a well defined world model (see

section 2), provided by the UMLS medical

knowledge base (McCray and Nelson, 1995), in

order to define the semantic category and the level

of specificity of the extracted terms. This is a kind of

semantic analysis.

The user profiles and the medical documents are

represented with vectors, which are typical IR

representation models. The vectors enclose the

semantic categories of the medical terms and their

associated values. The representations are then

compared calculating the cosine similarity of the

vectors (Salton and Buckley, 1988), which is also a

typical IR technique. Tests conducted with the

system (Teufel et al., 2001) have shown that the

semantic analysis enhances precision and recall of

the system, compared to standard IR techniques.

4 TEMPLATE-BASED

APPROACH

Template-based QA extends the pattern matching

approach and exploits a collection of manually

created question templates, i.e. questions which have

open concepts to be filled with data instances,

mapped into the conceptual model of the knowledge

domain. The templates generally cover the most

frequently asked questions (FAQs) of the domain

(Sneiders, 2002b), and can be either static, where

each template is a question linked to a piece of static

text, or dynamic and parameterized if they cover a

structured database (Sneiders, 2002a). A question

template is viewed as a predicate with variable and

fixed parameters:

∃ data

,…data

:Q(fixed

,…fixed

,variable

,…

variable

)

During the process of matching a template to a user

question, the fixed parameters (fixed

,…fixed

) are

bound to the user question. If there are database data

instances (data

,…data

) that fit the variable

parameters (variable

, …variable

) and make the

statement Q true, then these data instances constitute

the answer. This approach has been utilized on a

medical portal aiming at providing cross language

QA in matters of psychology and psychotherapy

(http://www.web4health.info).

A similar approach, which focuses on classifying

user questions with the help of pre-determined

semantic patterns, is applied in a feasibility study for

creating a QA prototype for the oral surgery domain

(Jacquemart and Zweigenbaum, 2003). The patterns

are created with triples that contain two concepts

and their relation (Concept A – Relation – Concept

B). The relation between the concepts is defined

with the help of the UMLS Semantic Network

(McCray and Nelson, 1995).

The Medline Button system (Cimino et al., 1992)

tries to automate the question generation process

creating semantic patterns of concepts that occur

frequently in user questions. The system then

instantiates the generic concepts in the templates

with terms that are specific for the search context

and user interests. For instance the template “Does

<procedure> cause <disease>?” is instantiated to

“Does chest x-rays causes cancer?” if the user is

interested in those topics.

The PICO-format (Sackett et al., 2004), utilized

in several medical QA systems (Niu et al., 2003,

Demner-Fushman and Lin, 2005), consists of

templates that classifies NL input with the help of a

conceptual structure that represent the key elements

of clinical questions: Problem (the primary problem

of the patient), Intervention (medication or

therapeutic procedure), Comparison (of the actual

intervention to other possible interventions) and

Outcome (the effect of the intervention).

A system that implements IR and templates-

based techniques is the EPoCare QA system (Niu et

al., 2003). Candidate answers are first retrieved with

standard IR techniques and then classified with the

PICO format, prior to being matched to PICO-

formatted user questions. The system also tries to

classify the relations between the PICO conceptual

units, for instance individuating cause-effect

relations between interventions and outcomes.

5 QA TECHNIQUES

AND THE MEDICAL

DOMAIN - DISCUSSION

As mentioned in section 3, the IR approach

distinguish the expected answer type (e.g. person,

place or time) with the help of the so-called “wh-

words” in the user question (e.g. who, where, when).

Niu et al. (2003) states that this classification is not

AUTOMATED QUESTION-ANSWERING TECHNIQUES AND THE MEDICAL DOMAIN

209

appropriate for the medical domain for the following

reasons:

1) Questions about patient care usually deal with

diagnosis, treatments, prognosis and outcome of the

treatments (Richardson et al., 1995). This require a

methodology for identifying answer types that is

different from the traditional approach utilized for

generic “factoid” QA systems.

2) Answers to “when”-questions in medical area are

usually related to relative time (Q: “When should I

eat my medicine?” A: “One hour before lunch”)

rather than absolute time/dates, which is typical for

generic QA systems (Q: “When was America

discovered?” A: “1492”), or address a clinical

condition (Q: “When should I see a therapist?” A:

“You should consider professional advice if your

personal problems are affecting your quality of life

and social functions at work or at home for more

than a month”). This requires a deeper semantic

interpretation of the user question.

3) Yes-no questions, i.e. questions that require yes or

no answer (e.g. “Is cognitive behaviour a good

therapy method for a person suffering from anxiety

disorder?”) are not considered by systems that focus

on “wh”-questions.

Furthermore IR techniques extract answers

containing words that are present in user questions,

without considering contextual information in the

text that could be relevant to provide and justify

answers (Niu and Hirst, 2004, Laurent, Seguela and

Negre, 2006). In medical applications correct

answers may be missed or incorrect answers may be

retrieved if contextual information is not understood,

since the context may provide more evidence, clarify

or even contradict the extracted snippets (Niu et al.,

2003).

Deep NLP-based and Template based QA are the

techniques that better fit QA in medical matters.

Both approaches handle more advanced types of

questions that implies understanding of their context,

such as yes-no questions, and have shown better

results when it comes to requests for “advice-giving”

(e.g. “How to…” questions) since they perform a

semantic interpretation of user input (Andrenucci

and Sneiders, 2005, Laurent, Seguela and Negre,

2006). For example semantic analysis proved to

improve the disambiguation of causal questions that

involves reasoning (e.g. “How does cancer

develop?”) and the precision results of question

answering (Girju, 2003).

In the template based approach the interpretation

is done manually, individuating for each single

template the concepts that cover a part of the

conceptual model of the knowledge domain. In the

NLP approach the interpretation is done

automatically by the system as questions are asked,

mapping user questions and candidate answers into a

formal semantic representation.

Unlike IR enhanced by shallow NLP, those

techniques do not rely on data redundancy, which is

more likely to be useful in large, open domains

(Molla et al., 2003). Information in restricted

domains, such as the medical one, is usually well

structured and it is unlikely that answers to the same

question are redundantly present in several places of

the information source (Niu et al., 2003). Deep NLP

and Template based QA are the techniques that are

more often utilized to form interfaces to structured

data (Andrenucci and Sneiders, 2005).

However there are some important differences

that determine the context of application of the two

afore-mentioned techniques. The NLP approach

provides a natural flow in the user-computer

dialogue that resembles human-to-human

communication, thanks to the implementation of

realistic discourse planning models; see for instance

(Buchanan et al., 1995). NLP-based systems may

also implement dialectical argumentation techniques

in order to be more persuasive while giving advice

in health matters. One example is the DAPHNE

system (Cawsey, Grasso, and Jones, 1999), which

provides advice for the promotion of healthy

nutrition and implements a persuasive

conversational model based on providing supports

for its claims (“People who eat more fruit have less

diseases”) and anticipating possible counter

arguments and exceptions (“Although you may not

like all types of vegetables…”).

So in dialoguing or counselling matters that have

to resemble the patient-doctor communication, the

NLP approach is preferable. The NLP-approach also

delivers more reliable answers in comparison to the

other approaches (Andrenucci and Sneiders, 2005,

Molla et al., 2003, Teufel et al., 2001). For example

in Power Answer (Moldovan et al, 2003), the best

performing system for TREC 2004 and 2005, a logic

proof based on abductive justifications is performed

among the candidate answers prior to presenting the

valid answers to the users, enhancing the quality and

reliability of the results. Power Answer achieved an

accuracy of 70% while other medical systems

implementing approaches similar to the template

based approach achieved 60% (Jacquemart and

Zweigenbaum, 2003) of accuracy. Among IR

systems, Persival (McKeown et al., 2001) achieved

precision results that varied between 65 % and 89 %,

however IR techniques were supported by syntactic

and semantic analysis.

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So in cases where the reliability of the answers is

vital, systems enhanced by NLP approach are

preferable; for instance medical systems that support

practitioners in their decision making process and

that provide evidence for the suggested answers (Lin

and Demner-Fushman, 2005); the so-called

evidence-based medicine (Sackett et al., 2000).

A major drawback of this approach is that

development and maintenance of NLP systems are

complex and require highly qualified personnel such

as programmers, knowledge engineers and database

administrators. For example when the NLP QA

needs to be adapted to multi-lingual environment,

changes needs to be applied to the whole linguistic

module, which includes the lexicon, the world

model, the semantic interpreter and the syntactic

parser (Androutsopoulos, Ritchie, and Thanisch,

1995). Another drawback is that this approach is

computationally intensive and requires high

processing time, which makes it difficult to adapt it

to the Web (Rinaldi et al., 2004, Hartrumpf, 2006).

Template-based question answering is the most

viable approach when it comes to medical

information portals on the Web (Andrenucci and

Sneiders, 2005). This is due to the following

characteristics: 1) its suitability to support multi-

lingual content, 2) the relatively easiness of

maintenance, 3) its capacity to solve linguistic

ambiguities such as word sense disambiguation

without computationally expensive software, 4) and

its capability to return answers in different formats.

The suitability to support content in several

languages has a simple explanation: user questions

are matched against question templates that match

different interpretations of the same question and

contain individual lexicons; this implies that it is

only necessary to change individual templates to get

a multi-lingual matching.

Template-based QA systems are also easier to

manage since they do not require rare skills: the

administrator must only have knowledge of the

subject domain and possess basic linguistic skills

(Sneiders, 2002a).

Thanks to the usage of multiple lexicons, i.e.

small individual lexicons attached to each template,

linguistic ambiguities are solved at the micro-level

rather than at the macro-level. Small lexicons

identify mutually exchangeable words (synonyms

and their grammatical forms) for every concept

within the narrow context of a given template/

document, rather than in the context of the whole

knowledge domain (Sneiders, 2002a), which is

typical for the deep NLP approach. This makes the

individuation of word meanings in different contexts

an easier and less error-prone process (Sneiders,

2002b, p. 262).

The template-based approach supports also the

retrieval of answers in a variety of multimedia

forms, such as spoken languages, audio-files and

imagery (Andrenucci and Sneiders, 2005, McKeown

et al. 2001).

The Template based approach has a high recall

level, which fits users who are interested in

retrieving complete sets of answers rather than few

very precise answers.

A drawback of this approach is that manual

creation of the templates is required. This is a

tedious process, which poses great consistency

demands among the persons who create the

templates. Another drawback is that the template-

based QA does not provide a natural flow in

user/system dialogue or provides dialogues of poor

quality. One of the first medical systems trying to

use templates while dialoguing with users was Eliza

(Weizenbaum, 1966), a conversational agent created

to simulate the responses of a psychotherapist. The

system did not contain any domain knowledge and

the templates utilized regular expression in order to

match user input and to create responses that

exploited keywords from the input sentences. This

resulted often in nonsense answers and nonsense

dialogues (Copeland, 1993).

6 CONCLUSIONS

This paper has discussed three main techniques

within QA and has pointed out the approaches that

are more suitable for medical applications: the deep

NLP approach and the template based approach.

The template based approach is the most viable

commercially and fits Web-based medical

applications that are aimed at retrieving multilingual

content in different multimedia formats. Its high

recall level makes it the technique that fits users who

are more interested in retrieving complete sets of

answers rather than few very precise answers.

The deep NLP approach provides a dialogue that

better resembles the human-to-human conversation

and also delivers more reliable answers. It fits areas

where the precision of the retrieved information is

crucial, e.g. in decision-support or evidence-based

medicine.

IR enhanced by shallow NLP is more appropriate

as a search tool for larger or open domains as the

Web, since it exploits data redundancy. However it

can mainly retrieve factual answers unlike the NLP

and the template based approaches, which support

more complex types of questions such as requests

for “advice-giving”.

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