How Natural Language Processing Can Benefit Libraries

estonianmelonAI and Robotics

Oct 24, 2013 (3 years and 7 months ago)

60 views

Running head: HOW NA
TURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

1










How Natural Language Processing Can Benefit Libraries

Arianna L. Schlegel

Southern Connecticut State University












HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

2

Abstract

This paper will explore the
current lack of natural language processing (NLP) features in online
public access cata
logs (OPACs).
To do so, it will first examine advances recently made in online
search engine development which have incorporated NLP, and then will
look at the less
significant developments made in OPAC research which also involve NLP.
There are currentl
y
large discrepancies between the capabilities of online search engines and OPACs
, with the
former being several strides ahead of the latter
.
This paper
recognizes the differences, and
concludes with suggestions on how to
improve future research into OPAC
development.

These
include breaking away from the card catalog model, looking further into how humans approach a
search, and
incorporating NLP approaches, which might include dialogue between computer and
human,
more effective visual browsing,
and word as
sociation.

Keywords:
Natural language processing, OPACs, libraries.
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

3

Why is
N
atural Language Processing

Important for L
ibraries?

Researchers have been studying a
rtificial intelligence (AI) and its app
lication to libraries
for several decades, ever since com
puters began to play a large part in the organization of library
resources.
Artificial intelligence is the study of how to make computers imitate human beings as
closely as possible.

Natural
language p
rocessing (NLP) is a branch
of AI, wherein researcher
s
aim to gather knowledge on how human beings understand an
d use language so that computer

systems can be made to understand and manipulate natural languages (Chowdhury, 2003).


This
paper will attempt to emphasize how n
atural language processing

has
very

important potential in
enhancing the searching capabilities of

online library

catalogs.


Wha
t is NLP

and
How Does it Rela
te to
H
uman

L
anguage
?

Computers and humans simply do not speak the same language.
Due to their
radically
different makeup, su
ch a dis
connect is inevitable.
At their most basic, computers process in
binary, which means their

language building blocks consist of only two things:
electronic signals
in
either an

“on” or “off” state
.

Human, or natural, language
at its most biological level

is many
,
many

degrees more complex; science still does not

wholly

understand
how the brain processes
the spoken and written word.
However,
for years resear
chers have been
attempting to fi
nd a way
to reconcile the two: those studying artificial intelligenc
e

search
for ways in which to
program
computers so that machines

can process


understand and respond to


human language.

It is true

that

computers in this day and age work with higher
-
level programming
languages such as C++ and Java, languages

which em
ploy English words and thus
are much
closer to
human language

than is binary machine language.

Yet e
ven the most e
volved
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

4

programming language is not
fluently
readable by the human eye, particularly one not trained in
computer science.

Computer languages a
re

similar to human languages in that they are used to instruct and
inform the machine (insomuch as a computer can be considered “informed”).
Therefore, the
study of NLP is both relevant and full of potential.
In this, the twenty
-
first century, with the
vast
speed and processing power of computers, and the potential for seemingly unstoppable gains in
computing
capabilities
,
it is feasible to believe that computers can become so advanced that they
can largely emulate the incredibly complex
language

process
es

of a human being.


What Can NLP Do for L
ibraries?

One branch of NLP research attempts to
develop computer interfaces that can take human
search queries in natural languages and process them, without requiring the user to be
constrained by “search terms”

or concerned with word ambiguities, for instance.
Ideally, a user
should be able to use a search engine as he would a human resource, such as a reference
librarian.
Contrary to their nature,
p
eople in the twenty
-
first century have been forced, in their
use of computerized search engines, to conform to certain search standards which require only
snippets of natural language

and very unnatural grammars
.

In a library catalog search, for
example, one might enter only a one
-
word subject, even if trying to an
swer a much more
complex and specific question.
On the other hand, if one were to ap
proach a reference librarian,
an entire sentence,

revealing the true intent of the inquiry
,

would most likely be used.

Therefore,
significant research has been done in th
e field of NLP, with the intent of making
computerized

card catalog searches much more amenable to human language queries.

HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

5

Currently, most O
nline Public Access Catalog (OPAC)

software
found in libraries is

considered “second generation,”

offering
subject
s
earches

which look

solely for the occurrence
of a single search word as it appears in the title of a work or in the
subject headi
ngs associated
with that work (Antelman, Lynema, & Pace, 2006
; Borgman, 1996
).
This means that
OPACs
remain on almost the same

level as physical card catalogs, in terms of how limited a user’s
options are when trying various approaches in researching a topic.
According to Loarer (1993),
most OPAC searches are for subjects, and the results come only from a simple word search
perf
ormed on the catalog.
However, many believe that there is still a large, untapped potential for
OPACs to offer much more user
-
friendly and intuitive searches.


A
Very Brief History of Online Catalogs

As
w
ould be expected,
most online card catalogs began as

just that: online replications of
the capabilities of the physical card catalog.

Others
allow
ed

users to u
tilize

Bool
ean search
terms to combine two or more words in one query.

Second
-
generation designs, which

appeared
in the 1980s and

are still the sta
ndard today, simply offered the combined functionality of the
two previous kinds of OPACs.

Natural language processing did not factor into these systems.
Therefore, it is clear that most libraries continue to
offer very
primitive online catalogs, despite

the
advances occurring all around
them
in other areas of search engine research, most notabl
y
that of internet
searching
.
Library patrons have become used to search engines like Google, and
therefore

approach an OPAC with the expectations of a similar

us
er

experience.
Unfortunately,
Antelman et al. (2006) point out that online catalogs have remained largely stag
nant for close to
twenty years,
failing to keep up with
advances in
search technology
due
largely to the profession
overlooking

the problem, for
various reasons.



Loarer (1993) even pointed

out that “OPAC” is
,
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

6

ironically,

a homonym of the French pronunciation of “opaque,” and remarks on the unfortunate
yet largely apt choice of acronyms.

Clearly there is
much that can be done to
enhance the onlin
e
catalog experience
.
We will first take a look at how online search engines are beginning to take
advantage of natural language processing, and then examine how these developments
might be
applied to better the OPAC user experience.


NLP and online sear
ch engines

It is becoming very clear that online searching is replacing library
searching

for many
people, even
those who are
regular patrons of the library.
This is largely due to the belief that
Internet search engines produce more numerous and more rel
evant

query

results than do OPACs.
While Google is currently the most popular search engine, there are several new NLP search
engine developments on the horizon that have great potential, and should be considered when
redesigning OPAC search engines.


Go
ogle

While Google is not an NLP
-
based engine at its heart, it presents an interesting case study
to begin with, as
it
consists of

a very interesting blend of more robust searching powers than
OPACs, while

still constraining the searcher to
very limited a
pp
roaches. As Fox (2008) pointed

out, Google has its users typing in
, on average,

2.8 words that represent the most vital points

of a

search quer
y; therefore, if one were
interested in the learning the winner of the Westminster
Kennel Club dog show in 1932,

a typical Google search string
might

be
“Westminster dog show
winner 193
2.”
A relevant and useful result is returned at the top of Googl
e’s results page, but the
user
is
required to
form

the

query in a very unnatural way.
One

certainly would not walk in
to a
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

7

library and use the same phrase, sans
semantic fillers

of any kind, when requesting the
above
information of a librarian; it simply would not make sense in
a face
-
to
-
face
human interaction.

However,
when using a traditional OPAC
, the search would
mos
t likely

start even more
generally, with a search perhaps under “dog shows” or “Westminster.”
Therefore, Google does
allow for more specific searches, but the engine cannot process true requests, and therefore
remains limited in its capability to produce
the most relevant results for a user. Its results are

also

listed in order of relevance based on what Google’s algorithm
deems

most important;
the
user is largely
shuttled into the pages which ha
ppen to have the
best results
according to the
search engine
’s “more links equals more importance” approach
.
Therefore, it is feasible that a
searcher might not ever find the information
he is

looking f
or; even if Google returns tens of
thousands of results, most users do not tend to
look beyond the first several
pages of results
before
trying a new search or simply giving up the query as unanswerable (
Ibid
).
So while
Google is a good starting point for NLP development, it certainly has a long way to go before it
can be considered to be processing

true

natural lan
guage.


WAG

WAG
,

deemed an online “answer extraction system” by Neumann and
Xu

(2004),

was
developed by a team of German researchers, with the goal of answering
questions
, rather than
offering results relat
ed to a search term or phrase.
This search engin
e, then, much more closely
resembles the work of a reference librarian, and offers a peek at what an OPAC might look like
were it to blend reference with general card catalog capabilities.
The engine allows for the use of
more structured queries, which wo
uld typically include specific question words such as “who,”
“when,” “where,” etc.
The search engine uses NLP to determine what type of response is being
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

8

looked for; if the query contains “when,” the user is most likely looking for a st
ring containing a
d
ate format, and
phrases

formatted only

in that

particular pattern are examined.

WAG

also recognizes

an NLP concept

called Named Entities (NEs), which
are generally
proper nouns, and dynamically cre
ates a lexicon for the search engine to reference while it
is
generating its results. This means
, for instance,

that
a

user could enter a search query which
included a full name, and WAG would recognize that i
t sh
ould scan web pages for a
n answer
related not only to that

full n
ame, but also any information locate
d near either
the first or last
name
s
,
which might appear

separately from
one an
other
.
Such semantic recognition is a feature
which

online

search engines as well as OPACs
currently do not handle well; NE lexicons would
be very

beneficial to a library patr
on’s
search
for references related to

a proper noun.


Alpha

Alpha is the brainchild of
mathematician
Steph
en Wolfram, and his

goal with this project
i
s

to create a search engine that returns real answers, as opposed to links

that are simply

related
to
the

more general search topic (Levy, 2006)
.
Wolfram’s

approach

to creating such a relevant
database
i
s to
utilize customized databases which
can be scanned

in order

to answer specific,
English
-
language questions
quantitatively, returning numbers
and
other in
formation

that
is

pulled from
the databases to create “mini
dossiers” on the subject being queried

(Ibid)
.
Therefore, one would receive a numeric answer to a quantitative search question;
i.e.,
the real
-
time, current distance between Earth and the sun, ca
lculated from one of the numerous databases,
as opposed to results which simply list pages that may
or may not

contain the desired
information.

HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

9

Of course, this approach clearly requires a large amount of
background

work to
implement; Wolfram himself is c
reating many of the databases from which his search engine’s
answer
s

would be pulled. However, the
concept is an important one to consider when
determining best practices for NLP searches.
It is possible to organize information so that it is
truly
more a
ccessible, and an OPAC developer might consider

building

more customized
databases in order to enhance a search engine’s performance.


Haki
a

Another very pertinent development in the field

of online

search engines
can be

seen in
the
developme
nt of Haki
a, w
hich clai
ms to be a
true
semantic search engine
.
Ha
kia
’s goal is to
return results that are credible and usable, but not necessarily popular

by the same standards as
Google or Yahoo!;

today’s
more common search engines have decreed

“popular”
to mean

a web

page
which
has many links pointing to it

(Fox, 2008)
.

On its website,
Ha
ki
a
(2007)
explained

its
various NLP approaches, which offer some key insights into how to perhaps improve upon the
Google searches of today.
For instance, Ha
ki
a bases its
search re
sults on concept matching,
rather than on
a simple
keyword matching or popularity ranking (Ibid).
The search engine
claims to use a more advanced version of

a web crawler for

indexing web pages, which can
i
dentify concepts through semantic
, NLP

analysis.


Once the concepts are identified, the engine
can produce results for a
human
searcher that are subcategorized from a more general topi
c
search, and thus allow the user to

choose to focus the search on more specialized results.

Other special features of
Haki
a include the NLP ability to identify equivalent terms for a
more responsive search (i.e., recognizing that another word for “treatment” could be “cure”),

as
well as the
ability to
group brand names under broader umbrella categories, such as

recognizin
g

HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

10

“Toyota” as a type of “car.”
And, like Google, Haki
a does offer suggestions and corrects
spelling errors in searches


a feature whi
ch OPACs
generally do not
offer (Ibid).

A final human touch in this search engine is that it

offers an

option
to consider

only

“credible” sources, which are in fact recommended to
Haki
a

by actual librarians, thus
und
erscoring the importan
t role

that an information scientist
still must play in
recognizing and
promoting
appropriate sources.
This is an important
consideration
in

the development of any
OPAC software, and it is gratifying to see such a point being made by an online search engine.

At the moment, Ha
k
i
a is still a very limited search engine, and therefore hasn’t gained
wide usage, but it holds a lot of promise, and
certainly can offer many pointers to developers of
an NLP
-
based OPAC system.


Advanc
es in OPAC Development Using NLP

With all of the developments taking place in the field of

online search engine
s with
respect to NLP, one might understandably think that on
line catalogs are following suit, and
becoming much more flexible and relevant. However, this is largely not the case.
OPACs tend
to remain
based solidly in the
concept of physical card catalogs, offering very limited searching
on traditional categories
such as author, title, or subject.
OPAC

designers are overwhelmingly
not taking advantage of many of the very useful
search engine
tools already in place, nor do they
seem to be exploring the vast amount of research into how natural language processing co
uld
better serve the libr
ary user. Borgman (1996) pointed

out that
online catalogs do not seem to be
even attempting to understand search behavior, despite library catalogs being pioneers in the
field

of computerized research
.
Fortunately, however,
there

are several small enterprises


most
not yet being used by the majority of libraries


which attempt to reconcile NLP and OPAC
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

11

searching, with what appears to be
notable

success.

While remembering that much is still left to
be developed, we will now take

a look at of some of the strides taken in OPAC development.


Okapi

The Center for Interactive Systems Research (CISR) was formed in 1987
, with the intent
of studying

computerized

information retrieval (IR).
Much of its early resear
ch focused on
OPACs; in

the late 1980s, a program named Okapi was introduced and the group began working
with the product via
Text Retrieval Conference (TREC) competitions. The competitions
encouraged teams to further advance the NLP

capabilities of the software (Department of
Information Science, City University of London, 2008).

In
2000
, Microsoft Research Cambridge took part in the competitions, and made some
si
gnificant
inroads in the development of more user
-
friendly OPACs,

utilizing a combination of
the Okapi IR engin
e a
nd NLPWin, it
s

proprietary

NLP system (Elworthy, 2000).
The group
engineered a question
-
answering system that was able to take questions as input, parse them to
gather the meaning, and then find answers by locating sentences that contained similar phrasin
g.
The system took advantage of the common knowledge that questions are often formed in very
similar ways; many start with the same structure, such as “who is” or “
when was,” and are then
followed by the important search cues, which th
e software could the
n exploit.
Additionally,
question words
can

be associated with what response is expected; for instance, “who is” requires
a person in its answer, “where” requires a location, and “when” demands some f
orm of a time
response. Often, these keywords are also

followed by even
more focused

specifics, such as
“what country” or “what year,” which considerably narrows down the scope of potential results.
Thus, the software could easily parse the majority of questions, and then
grab

only the specific
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

12

information i
t required
in order

to locate pertinent answers.
While this software did not perform
optimally during the competition tests,
the concepts that it
examined
present some truly notable
considerations for future OPAC development.


Visualization software

Luthe
r, Kelly, and Beagle (2005)
recognize
d

that
search engines often return such large
data sets that the majority of
the results are unusable


largely because they are never even
viewed.
One of the biggest problems
with

search engines in general, and with O
PACs
specifically, are that there are so many ways to lo
ok at a subject.
A library patron’s needs vary
drastically from person to person, and
even between several searches performed

all

by the same
patron.
Where one is an expert another might be a novice
, but when one begins to research a
subject, OPACs require every user to start

with very broad, sweeping catego
rizations of the
information

of which

they are in search.
Additionally, the broad subject searches usually return
a mixture of results that vary

drastically in
depth, coverage, and
approach.
While this
is
sometimes

useful


as
when a patron does not know specifically
for what they are looking



often a user has a
predefined

query
or search term
in mind, but must use a drill
-
down searching
method

in order to locate
a useful

answer.
Luther et al. (2005) recognized the vast differences
between the approach to every search

engine query, and examined visualization software as a
possible
means of ameliorating

the discrepancies
.
Visualization

programs
attempt to work more
closel
y with how the human brain works and how it processes linguistics
, allowing users to
follow certain paths, associate conce
pts, and backtrack
,

using interfaces which present data
clouds and subh
eadings for the user to click throug
h.
The software also uses NLP to produce
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

13

topical clusters which associate certain words or concepts

more
along the lines of

how the human
mind does so
.

Certain OPAC
-
specific software is being developed
which attempt
s

to incorporate

these
novel approache
s to searching. Notably, OCLC is working in conjunction with Antarctica
Systems, Inc. to create a
visual interface to the OCLC’s Electronic Books database.
AquaBro
w
ser, which has been adopted by institutions including Harvard and
Miami
-
Dade Public
Librar
y System,

is a
nother well
-
known product which utilizes vis
ual language s
earching.
In
recent years, s
everal other prominent library database software companies have been
exploring
the applicability of visual search to their

own products

(Ibid)
.

Clearly th
is is an idea that is
catching on.


Endeca

Antelman et al. (2006) evaluated
North Carolina State University’s

recent
adoption

of
a
next
-
generation OPAC software called Endeca. While not strictly an NLP search engine, this
program allowed the university to

upgrade from their older OPAC to
offer a much better tool for
finding relevant resources.
Specifically, the university was eager to replace its keyword search
engine with software that had been developed for use with large commercial websites, as they
fo
und that their students were more familiar with those types of searches.

The software offer
s

many features that the university did not find in traditional OPAC software, such as the ability to
assign search indexes different relevance rankings, and to
cor
rect user typos or misspellings.
The
auto
-
correct in Endeca is also much more relevant, as it pulls its
suggestions

from a
self
-
compiled list of frequently used terms
, rather than simply referring to

a dictionary for options.
These types of responses fro
m search engines
, which take into account the way a human is more
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

14

likely to approach a search,

and which compile some of their searching mechanisms from the
user herself,
are
much more desirable
than
the
basic keyword searches

most used in
OPACs
today
.


Co
nclusion

How Advances in NLP Can Benefit OPACs

NLP is still in its infancy in many ways, and it may be a long time before computers can
understand natural language
in any sense near the depth that

humans
are able
.
However, it is
clear from the above cases

that many significant advances are being made in the field which
might be very promising for the future of online catalog searching.
In an ideal world, an OPAC
would be

able to
answer a short, specific question while pointing the user towards further
res
ources



much like

the role of

a reference librarian
.

There is currently a vast difference
between an interaction with a human and
an interaction with a computer, and unfortunately, most
scenarios are leaning towards “computer
-
friendly” rather than “user
-
friendly” interfaces
(Chowdhury, 2003
; Borgman, 1996
). This means that humans are adapting themselves to what a
computer expects, rather than the other way around.
Such change

does not need to happen.
Research continues to take place in the field of NLP
, and significant strides continue to be made.
OPAC designers must
begin

to explore and apply these advances to their products, or they risk
losing their already crumb
ling hold on patron use.

Loarer (1993) agreed

that more user
-
friendly
developments in O
PACs, particularly in language processing, will have users in
educational,
processional, and domestic environments

taking
significantly more
advantage of the software
.


It is true, however, that much remains to be developed in the field of NLP. The nuance
s
and complexities of human language are certainly
difficult to map to a binary system of ones and
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

15

zeros
.
Most NLP research has been done solely in closed, controlled settings, and has not been
applied to real
-
world scenarios.
It is understandable, then,

that only tried and true inroads into
better searching software have been adopted by OPAC developers.
However, applying NLP
advances in search engine design
to OPAC design is a necessary next step

towards more
technologically
-
friendly
libraries
.
Followi
ng are some
suggestions

for future development in
NLP and
OPAC design

which were
gathered throughout my research
:



Study search behavior:

one cannot design a system that is more accommodating to
searching if human search approaches are not understood.

For
instance, users
tend to search in stages as they refine their queries, and sometimes use searching
as a way to formulate an actual question.
Consider this when designing systems.

Offer a way for a user to incorporate earlier, related s
earches into the cu
rrent
search (Borgman, 1996).



Separate

altogether

from the card catalog model; it was designed for a specific
physical space which no longer constrains the computer
ized

OPAC, and therefore
software designers must literally think outside of the physical car
d catalog

(Ibid)
.



Move on from query
-
matching systems. They were designed for use by skilled
searchers, such as librarians, not for use by untrained library patrons
. Other
approaches to information
-
gathering

are more natural for the layman user

(Ibid)
.

Consider question
-
parsing mechanisms that refine or enlarge the user’s query
either automatically or in a dialogue with the user,
using
algorithms that

take the
query’s semantics into consideration

(Loarer, 1993; Cenek, 2001).



Make sure help is readily ava
ilable to the user, offering

aid on how to use the
software

as well as

how to perform an effective sea
rch

(
Loarer, 1993
). Users will
HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

16

be especially unfamiliar with how to best utilize NLP searches when those
become more widely
adopted
; they will need instr
uction that is easy to access,
understand, and remember

(Kreymer, 2002)
.

Additionally,
offering the option to
format the organization and display of results to suit the searcher’s needs would
further accommodate the objectives of a search.



Allow for bette
r random browsing, much like the
opportunities which physical
card catalogs used to offer a patron: the chance to stumble

serendipitously
across
an interesting subject, or to refine a search with unfamiliar subcategories.



Don’t make users guess which subje
ct headings are being used in the OPAC, nor
how subheadings are associated with one another. Allow fo
r more visual
conceptualization, which is mor
e in line with how humans think and learn.

These are just some of what ought to be considered when developing

a more NLP
-
oriented
OPAC system.
It is impossible to
know

how many more facets might be discovered in the
process of development
, a process
which should ideally include usability surveys and testing, to
better understand what a library patron wishes to g
et from a search engine.

Natural language
processing is just one small part of what needs to go into the
improvement of
current
OPACs.
Yet it remains so very important in the
further development of libraries

which ar
e based, at their
heart, on the ideal
of
freely
sharing human knowledge


through language.

HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

17

References

Antelman, K., Lynema, E., & Pace, A.K. (2006, September). Toward a twenty
-
first century
library catalog.
Information Technology
&

Libraries, 25
(3), 128
-
139.

Borgman, C.L. (1996, July). Why a
re online catalogs still hard to use?
Journal of the American
Society for Information Science, 47
(7), 493
-
503.

Cenek, P. (2001, June).
Dialogue
interfaces for library systems
. In proceedings of FIMU
-
RS
-
2001
-
04.


Chowdhury, G
.
G. (2003). Natural language pr
ocessing. In B. Cronin (Ed.),
Annual Review of
Information Science and Technology, 37,
51
-
89. Medford, NJ: Information Today.

Department of Information Science, City University of London. (2008).
CISR


Mission
statement.
Retrieved from
http://www.soi.city
.ac.uk/organisation/is/research/cisr/

Elworthy, D. (2000).
Question answering using a large NLP
. Proceedings of the Ninth T
e
xt
Retrieval

Conference (TREC 2000),
355
-
360.

Fox, V. (2008, January).
The promise of natural language search.
Information Today, 25
(1), 50
-
50.

Hakia, Inc. (2007).
Technology
. Retrieved from
http://company.hakia.com/technology.html

Kreymer, O. (2002). An evaluation of help mechanisms in natural language processing.
Online
Information Review, 26
(1), 30
-
39.

Levy, S. (200
9, May 22). Steve
n Levy on the answer e
ngine, a radical new formula for web
search.
Wired Magazine, 17
(6). Retrieved from
http://www.wired.com/techbiz/people/magazine/17
-
06/ts_levy

HOW NATURAL LANGUAGE PROCESSING CAN BENEFIT LIBRARIES

18

Loarer, P.L. (1993). OPAC: Opaque or open, public, accessible, and co
-
operative? Some
develo
pments in natural language processing.
Program: Electronic Library and
Information Systems, 27
(3), 251
-
268.

Luther, J., Kelly M., & Beagle, D. (2005, March 1). Visualize this.
Library Journal.
Retrieved
from http://www.libraryjournal.com/article/CA504640.h
tml

Neumann, G., &
Xu, F
. (2004, June). Mining natural language answers from the web.
Web
Intelligence & Agent Systems,
2
(2), 123
-
135.