INTRODUCTION
Natural Language Processing (NLP) is the computerized approach to analyzing text that
is based on both a set of theories and a set of technologies. And, being a very active area
of research and development, there is not a single agreed-upon definition that would
satisfy everyone, but there are some aspects, which would be part of any knowledgeable
person’s definition. The definition I offer is:
Definition:
computational techniques for analyzing and representing naturally occurring texts
at one or more levels of linguistic analysis for the purpose of achieving human-like
language processing for a range of tasks or applications.
Several elements of this definition can be further detailed. Firstly the imprecise notion of
Natural Language Processing is a theoretically motivated range of ‘range of computational techniques’
techniques from which to choose to accomplish a particular type of language analysis.
is necessary because there are multiple methods or ‘Naturally occurring texts’
oral or written. The only requirement is that they be in a language used by humans to
communicate to one another. Also, the text being analyzed should not be specifically
constructed for the purpose of the analysis, but rather that the text be gathered from actual
usage.
The notion of
the fact that there are multiple types of language processing known to be at work when
humans produce or comprehend language. It is thought that humans normally utilize all
of these levels since each level conveys different types of meaning. But various NLP
systems utilize different levels, or combinations of levels of linguistic analysis, and this is
seen in the differences amongst various NLP applications. This also leads to much
confusion on the part of non-specialists as to what NLP really is, because a system that
uses any subset of these levels of analysis can be said to be an NLP-based system. The
difference between them, therefore, may actually be whether the system uses ‘weak’ NLP
or ‘strong’ NLP.
can be of any language, mode, genre, etc. The texts can be‘levels of linguistic analysis’ (to be further explained in Section 2) refers to ‘Human-like language processing’
Artificial Intelligence (AI). And while the full lineage of NLP does depend on a number
of other disciplines, since NLP strives for human-like performance, it is appropriate to
consider it an AI discipline.
reveals that NLP is considered a discipline within ‘For a range of tasks or applications’
goal in and of itself, except perhaps for AI researchers. For others, NLP is the means for
points out that NLP is not usually considered a Liddy, E. D. In Encyclopedia of Library and Information Science, 2nd Ed. Marcel Decker, Inc. accomplishing a particular task. Therefore, you have Information Retrieval (IR) systems
that utilize NLP, as well as Machine Translation (MT), Question-Answering, etc.
Goal
The goal of NLP as stated above is “
The choice of the word ‘processing’ is very deliberate, and should not be replaced with
‘understanding’. For although the field of NLP was originally referred to as Natural
Language Understanding (NLU) in the early days of AI, it is well agreed today that while
the goal of NLP is true NLU, that goal has not yet been accomplished. A full NLU
System would be able to:
1. Paraphrase an input text
2. Translate the text into another language
3. Answer questions about the contents of the text
4. Draw inferences from the text
While NLP has made serious inroads into accomplishing goals 1 to 3, the fact that NLP
systems cannot, of themselves, draw inferences from text, NLU still remains the goal of
NLP.
There are more practical goals for NLP, many related to the particular application for
which it is being utilized. For example, an NLP-based IR system has the goal of
providing more precise, complete information in response to a user’s real information
need. The goal of the NLP system here is to represent the true meaning and intent of the
user’s query, which can be expressed as naturally in everyday language as if they were
speaking to a reference librarian. Also, the contents of the documents that are being
searched will be represented at all their levels of meaning so that a true match between
need and response can be found, no matter how either are expressed in their surface form.
Origins
As most modern disciplines, the lineage of NLP is indeed mixed, and still today has
strong emphases by different groups whose backgrounds are more influenced by one or
another of the disciplines. Key among the contributors to the discipline and practice of
NLP are:
discovery of language universals - in fact the field of NLP was originally referred to as
Computational Linguistics; Computer
representations of data and efficient processing of these structures, and; Cognitive
to accomplish human-like language processing”.Linguistics - focuses on formal, structural models of language and theScience - is concerned with developing internal Psychology
has the goal of modeling the use of language in a psychologically plausible way.
Divisions
While the entire field is referred to as Natural Language Processing, there are in fact two
distinct focuses – language processing and language generation. The first of these refers
to the analysis of language for the purpose of producing a meaningful representation,
while the latter refers to the production of language from a representation. The task of
Natural Language Processing is equivalent to the role of reader/listener, while the task of
Natural Language Generation is that of the writer/speaker. While much of the theory and
technology are shared by these two divisions, Natural Language Generation also requires
a planning capability. That is, the generation system requires a plan or model of the goal
of the interaction in order to decide what the system should generate at each point in an
interaction. We will focus on the task of natural language analysis, as this is most
relevant to Library and Information Science.
Another distinction is traditionally made between language understanding and speech
understanding. Speech understanding starts with, and speech generation ends with, oral
language and therefore rely on the additional fields of acoustics and phonology. Speech
understanding focuses on how the ‘sounds’ of language as picked up by the system in the
form of acoustical waves are transcribed into recognizable morphemes and words. Once
in this form, the same levels of processing which are utilized on written text are utilized.
All of these levels, including the phonology level, will be covered in Section 2; however,
the emphasis throughout will be on language in the written form.
- looks at language usage as a window into human cognitive processes, and BRIEF HISTORY OF NATURAL LANGUAGE PROCESSING
Research in natural language processing has been going on for several decades dating
back to the late 1940s. Machine translation (MT) was the first computer-based
application related to natural language. While Weaver and Booth (1); (2) started one of
the earliest MT projects in 1946 on computer translation based on expertise in breaking
enemy codes during World War II, it was generally agreed that it was Weaver’s
memorandum of 1949 that brought the idea of MT to general notice and inspired many
projects (3). He suggested using ideas from cryptography and information theory for
language translation. Research began at various research institutions in the United States
within a few years.
Early work in MT took the simplistic view that the only differences between languages
resided in their vocabularies and the permitted word orders. Systems developed from this
perspective simply used dictionary-lookup for appropriate words for translation and
reordered the words after translation to fit the word-order rules of the target language,
without taking into account the lexical ambiguity inherent in natural language. This
produced poor results. The apparent failure made researchers realize that the task was a
lot harder than anticipated, and they needed a more adequate theory of language.
However, it was not until 1957 when Chomsky (4) published
Syntactic Structures introducing the idea of generative grammar, did the field gain better insight into whether
or how mainstream linguistics could help MT.
During this period, other NLP application areas began to emerge, such as speech
recognition. The language processing community and the speech community then was
split into two camps with the language processing community dominated by the
theoretical perspective of generative grammar and hostile to statistical methods, and the
speech community dominated by statistical information theory (5) and hostile to
theoretical linguistics (6).
Due to the developments of the syntactic theory of language and parsing algorithms, there
was over-enthusiasm in the 1950s that people believed that fully automatic high quality
translation systems (2) would be able to produce results indistinguishable from those of
human translators, and such systems should be in operation within a few years. It was not
only unrealistic given the then-available linguistic knowledge and computer systems, but
also impossible in principle (3).
The inadequacies of then-existing systems, and perhaps accompanied by the overenthusiasm,
led to the ALPAC (Automatic Language Processing Advisory Committee of
the National Academy of Science - National Research Council) report of 1966. (7) The
report concluded that MT was not immediately achievable and recommended it not be
funded. This had the effect of halting MT and most work in other applications of NLP at
least within the United States.
Although there was a substantial decrease in NLP work during the years after the ALPAC
report, there were some significant developments, both in theoretical issues and in
construction of prototype systems. Theoretical work in the late 1960’s and early 1970’s
focused on the issue of how to represent meaning and developing computationally
tractable solutions that the then-existing theories of grammar were not able to produce. In
1965, Chomsky (8) introduced the transformational model of linguistic competence.
However, the transformational generative grammars were too syntactically oriented to
allow for semantic concerns. They also did not lend themselves easily to computational
implementation. As a reaction to Chomsky’s theories and the work of other
transformational generativists, case grammar of Fillmore, (9), semantic networks of
Quillian, (10), and conceptual dependency theory of Schank, (11) were developed to
explain syntactic anomalies, and provide semantic representations. Augmented transition
networks of Woods, (12) extended the power of phrase-structure grammar by
incorporating mechanisms from programming languages such as LISP. Other
representation formalisms included Wilks’ preference semantics (13), and Kay’s
functional grammar (14).
Alongside theoretical development, many prototype systems were developed to
demonstrate the effectiveness of particular principles. Weizenbaum’s ELIZA (15) was
built to replicate the conversation between a psychologist and a patient, simply by
permuting or echoing the user input. Winograd’s SHRDLU (16) simulated a robot that
manipulated blocks on a tabletop. Despite its limitations, it showed that natural language
understanding was indeed possible for the computer (17). PARRY (18) attempted to
embody a theory of paranoia in a system. Instead of single keywords, it used groups of
keywords, and used synonyms if keywords were not found. LUNAR was developed by
Woods (19) as an interface system to a database that consisted of information about lunar
rock samples using augmented transition network and procedural semantics (20).
In the late 1970’s, attention shifted to semantic issues, discourse phenomena, and
communicative goals and plans (21). Grosz (22) analyzed task-oriented dialogues and
proposed a theory to partition the discourse into units based on her findings about the
relation between the structure of a task and the structure of the task-oriented dialogue.
Mann and Thompson (23) developed Rhetorical Structure Theory, attributing hierarchical
structure to discourse. Other researchers have also made significant contributions,
including Hobbs and Rosenschein (24), Polanyi and Scha (25), and Reichman (26).
This period also saw considerable work on natural language generation. McKeown’s
discourse planner TEXT (27) and McDonald’s response generator MUMMBLE (28) used
rhetorical predicates to produce declarative descriptions in the form of short texts, usually
paragraphs. TEXT’s ability to generate coherent responses online was considered a major
achievement.
In the early 1980s, motivated by the availability of critical computational resources, the
growing awareness within each community of the limitations of isolated solutions to NLP
problems (21), and a general push toward applications that worked with language in a
broad, real-world context (6), researchers started re-examining non-symbolic approaches
that had lost popularity in early days. By the end of 1980s, symbolic approaches had been
used to address many significant problems in NLP and statistical approaches were shown
to be complementary in many respects to symbolic approaches (21).
In the last ten years of the millennium, the field was growing rapidly. This can be
attributed to: a) increased availability of large amounts of electronic text; b) availability
of computers with increased speed and memory; and c) the advent of the Internet.
Statistical approaches succeeded in dealing with many generic problems in computational
linguistics such as part-of-speech identification, word sense disambiguation, etc., and
have become standard throughout NLP (29). NLP researchers are now developing next
generation NLP systems that deal reasonably well with general text and account for a
good portion of the variability and ambiguity of language.
LEVELS OF NATURAL LANGUAGE PROCESSING
The most explanatory method for presenting what actually happens within a Natural
Language Processing system is by means of the ‘levels of language’ approach. This is
also referred to as the synchronic model of language and is distinguished from the earlier
sequential model, which hypothesizes that the levels of human language processing
follow one another in a strictly sequential manner. Psycholinguistic research suggests that
language processing is much more dynamic, as the levels can interact in a variety of
orders. Introspection reveals that we frequently use information we gain from what is
typically thought of as a higher level of processing to assist in a lower level of analysis.
For example, the pragmatic knowledge that the document you are reading is about
biology will be used when a particular word that has several possible senses (or
meanings) is encountered, and the word will be interpreted as having the biology sense.
Of necessity, the following description of levels will be presented sequentially. The key
point here is that meaning is conveyed by each and every level of language and that since
humans have been shown to use all levels of language to gain understanding, the more
capable an NLP system is, the more levels of language it will utilize.
(Figure 1: Synchronized Model of Language Processing)
Phonology
This level deals with the interpretation of speech sounds within and across words. There
are, in fact, three types of rules used in phonological analysis: 1) phonetic rules – for
sounds within words; 2) phonemic rules – for variations of pronunciation when words
are spoken together, and; 3) prosodic rules – for fluctuation in stress and intonation
across a sentence. In an NLP system that accepts spoken input, the sound waves are
analyzed and encoded into a digitized signal for interpretation by various rules or by
comparison to the particular language model being utilized.
Morphology
This level deals with the componential nature of words, which are composed of
morphemes – the smallest units of meaning. For example, the word
be morphologically analyzed into three separate morphemes: the prefix
preregistration canpre, the root registra,
across words, humans can break down an unknown word into its constituent morphemes
in order to understand its meaning. Similarly, an NLP system can recognize the meaning
conveyed by each morpheme in order to gain and represent meaning. For example,
adding the suffix
This is a key piece of meaning, and in fact, is frequently only evidenced in a text by the
use of the
Lexical
At this level, humans, as well as NLP systems, interpret the meaning of individual words.
Several types of processing contribute to word-level understanding – the first of these
being assignment of a single part-of-speech tag to each word. In this processing, words
that can function as more than one part-of-speech are assigned the most probable part-ofspeech
tag based on the context in which they occur.
Additionally at the lexical level, those words that have only one possible sense or
meaning can be replaced by a semantic representation of that meaning. The nature of the
representation varies according to the semantic theory utilized in the NLP system. The
following representation of the meaning of the word
predicates. As can be observed, a single lexical unit is decomposed into its more basic
properties. Given that there is a set of semantic primitives used across all words, these
simplified lexical representations make it possible to unify meaning across words and to
produce complex interpretations, much the same as humans do.
launch (a large boat used for carrying people on rivers, lakes harbors, etc.)
((CLASS BOAT) (PROPERTIES (LARGE)
(PURPOSE (PREDICATION (CLASS CARRY) (OBJECT PEOPLE))))
The lexical level may require a lexicon, and the particular approach taken by an NLP
system will determine whether a lexicon will be utilized, as well as the nature and extent
of information that is encoded in the lexicon. Lexicons may be quite simple, with only
the words and their part(s)-of-speech, or may be increasingly complex and contain
information on the semantic class of the word, what arguments it takes, and the semantic
limitations on these arguments, definitions of the sense(s) in the semantic representation
utilized in the particular system, and even the semantic field in which each sense of a
polysemous word is used.
Syntactic
This level focuses on analyzing the words in a sentence so as to uncover the grammatical
structure of the sentence. This requires both a grammar and a parser. The output of this
level of processing is a (possibly delinearized) representation of the sentence that reveals
the structural dependency relationships between the words. There are various grammars
that can be utilized, and which will, in turn, impact the choice of a parser. Not all NLP
applications require a full parse of sentences, therefore the remaining challenges in
parsing of prepositional phrase attachment and conjunction scoping no longer stymie
those applications for which phrasal and clausal dependencies are sufficient. Syntax
conveys meaning in most languages because order and dependency contribute to
meaning. For example the two sentences:
the dog.’
Semantic
This is the level at which most people think meaning is determined, however, as we can
see in the above defining of the levels, it is all the levels that contribute to meaning.
Semantic processing determines the possible meanings of a sentence by focusing on the
interactions among word-level meanings in the sentence. This level of processing can
include the semantic disambiguation of words with multiple senses; in an analogous way
to how syntactic disambiguation of words that can function as multiple parts-of-speech is
accomplished at the syntactic level. Semantic disambiguation permits one and only one
sense of polysemous words to be selected and included in the semantic representation of
the sentence. For example, amongst other meanings,
folder for storing papers, or a tool to shape one’s fingernails, or a line of individuals in a
queue. If information from the rest of the sentence were required for the disambiguation,
the semantic, not the lexical level, would do the disambiguation. A wide range of
methods can be implemented to accomplish the disambiguation, some which require
information as to the frequency with which each sense occurs in a particular corpus of
interest, or in general usage, some which require consideration of the local context, and
others which utilize pragmatic knowledge of the domain of the document.
Discourse
While syntax and semantics work with sentence-length units, the discourse level of NLP
works with units of text longer than a sentence. That is, it does not interpret multisentence
texts as just concatenated sentences, each of which can be interpreted singly.
Rather, discourse focuses on the properties of the text as a whole that convey meaning by
making connections between component sentences. Several types of discourse processing
can occur at this level, two of the most common being anaphora resolution and
discourse/text structure recognition. Anaphora resolution is the replacing of words such
as pronouns, which are semantically vacant, with the appropriate entity to which they
refer (30). Discourse/text structure recognition determines the functions of sentences in
the text, which, in turn, adds to the meaningful representation of the text. For example,
newspaper articles can be deconstructed into discourse components such as: Lead, Main
Story, Previous Events, Evaluation, Attributed Quotes, and Expectation (31).
Pragmatic
This level is concerned with the purposeful use of language in situations and utilizes
context over and above the contents of the text for understanding The goal is to explain
how extra meaning is
requires much world knowledge, including the understanding of intentions, plans, and
goals. Some NLP applications may utilize knowledge bases and inferencing modules. For
example, the following two sentences require resolution of the anaphoric term ‘they’, but
this resolution requires pragmatic or world knowledge.
and the suffix tion. Since the meaning of each morpheme remains the same–ed to a verb, conveys that the action of the verb took place in the past.-ed morpheme.launch is in the form of logical‘The dog chased the cat.’ and ‘The cat chaseddiffer only in terms of syntax, yet convey quite different meanings.‘file’ as a noun can mean either aread into texts without actually being encoded in them. This The city councilors refused the demonstrators a permit because
violence.
The city councilors refused the demonstrators a permit because
revolution.
they fearedthey advocated Summary of Levels
Current NLP systems tend to implement modules to accomplish mainly the lower levels
of processing. This is for several reasons. First, the application may not require
interpretation at the higher levels. Secondly, the lower levels have been more thoroughly
researched and implemented. Thirdly, the lower levels deal with smaller units of analysis,
e.g. morphemes, words, and sentences, which are rule-governed, versus the higher levels
of language processing which deal with texts and world knowledge, and which are only
regularity-governed. As will be seen in the following section on Approaches, the
statistical approaches have, to date, been validated on the lower levels of analysis, while
the symbolic approaches have dealt with all levels, although there are still few working
systems which incorporate the higher levels.
APPROACHES TO NATURAL LANGUAGE PROCESSING
Natural language processing approaches fall roughly into four categories: symbolic,
statistical, connectionist, and hybrid. Symbolic and statistical approaches have coexisted
since the early days of this field. Connectionist NLP work first appeared in the 1960’s.
For a long time, symbolic approaches dominated the field. In the 1980’s, statistical
approaches regained popularity as a result of the availability of critical computational
resources and the need to deal with broad, real-world contexts. Connectionist approaches
also recovered from earlier criticism by demonstrating the utility of neural networks in
NLP. This section examines each of these approaches in terms of their foundations,
typical techniques, differences in processing and system aspects, and their robustness,
flexibility, and suitability for various tasks.
Symbolic Approach
Symbolic approaches perform deep analysis of linguistic phenomena and are based on
explicit representation of facts about language through well-understood knowledge
representation schemes and associated algorithms (21). In fact, the description of the
levels of language analysis in the preceding section is given from a symbolic perspective.
The primary source of evidence in symbolic systems comes from human-developed rules
and lexicons.
A good example of symbolic approaches is seen in logic or rule-based systems. In logicbased
systems, the symbolic structure is usually in the form of logic propositions.
Manipulations of such structures are defined by inference procedures that are generally
truth preserving. Rule-based systems usually consist of a set of rules, an inference engine,
and a workspace or working memory. Knowledge is represented as facts or rules in the
rule-base. The inference engine repeatedly selects a rule whose condition is satisfied and
executes the rule.
Another example of symbolic approaches is semantic networks. First proposed by
Quillian (10) to model associative memory in psychology, semantic networks represent
knowledge through a set of nodes that represent objects or concepts and the labeled links
that represent relations between nodes. The pattern of connectivity reflects semantic
organization, that is; highly associated concepts are directly linked whereas moderately or
weakly related concepts are linked through intervening concepts. Semantic networks are
widely used to represent structured knowledge and have the most connectionist flavor of
the symbolic models (32).
Symbolic approaches have been used for a few decades in a variety of research areas and
applications such as information extraction, text categorization, ambiguity resolution, and
lexical acquisition. Typical techniques include: explanation-based learning, rule-based
learning, inductive logic programming, decision trees, conceptual clustering, and K
nearest neighbor algorithms (6; 33).
Statistical Approach
Statistical approaches employ various mathematical techniques and often use large text
corpora to develop approximate generalized models of linguistic phenomena based on
actual examples of these phenomena provided by the text corpora without adding
significant linguistic or world knowledge. In contrast to symbolic approaches, statistical
approaches use observable data as the primary source of evidence.
A frequently used statistical model is the Hidden Markov Model (HMM) inherited from
the speech community. HMM is a finite state automaton that has a set of states with
probabilities attached to transitions between states (34). Although outputs are visible,
states themselves are not directly observable, thus “hidden” from external observations.
Each state produces one of the observable outputs with a certain probability.
Statistical approaches have typically been used in tasks such as speech recognition,
lexical acquisition, parsing, part-of-speech tagging, collocations, statistical machine
translation, statistical grammar learning, and so on.
Connectionist Approach
Similar to the statistical approaches, connectionist approaches also develop generalized
models from examples of linguistic phenomena. What separates connectionism from
other statistical methods is that connectionist models combine statistical learning with
various theories of representation - thus the connectionist representations allow
transformation, inference, and manipulation of logic formulae (33). In addition, in
connectionist systems, linguistic models are harder to observe due to the fact that
connectionist architectures are less constrained than statistical ones (35); (21).
Generally speaking, a connectionist model is a network of interconnected simple
processing units with knowledge stored in the weights of the connections between units
(32). Local interactions among units can result in dynamic global behavior, which, in
turn, leads to computation.
Some connectionist models are called localist models, assuming that each unit represents
a particular concept. For example, one unit might represent the concept “mammal” while
another unit might represent the concept “whale”. Relations between concepts are
encoded by the weights of connections between those concepts. Knowledge in such
models is spread across the network, and the connectivity between units reflects their
structural relationship. Localist models are quite similar to semantic networks, but the
links between units are not usually labeled as they are in semantic nets. They perform
well at tasks such as word-sense disambiguation, language generation, and limited
inference (36).
Other connectionist models are called distributed models. Unlike that in localist models, a
concept in distributed models is represented as a function of simultaneous activation of
multiple units. An individual unit only participates in a concept representation. These
models are well suited for natural language processing tasks such as syntactic parsing,
limited domain translation tasks, and associative retrieval.
Comparison Among Approaches
From the above section, we have seen that similarities and differences exist between
approaches in terms of their assumptions, philosophical foundations, and source of
evidence. In addition to that, the similarities and differences can also be reflected in the
processes each approach follows, as well as in system aspects, robustness, flexibility, and
suitable tasks.
Process:
namely, data collection, data analysis/model building, rule/data construction, and
application of rules/data in system. The data collection stage is critical to all three
approaches although statistical and connectionist approaches typically require much more
data than symbolic approaches. In the data analysis/model building stage, symbolic
approaches rely on human analysis of the data in order to form a theory while statistical
approaches manually define a statistical model that is an approximate generalization of
the collected data. Connectionist approaches build a connectionist model from the data.
In the rule / data construction stage, manual efforts are typical for symbolic approaches
and the theory formed in the previous step may evolve when new cases are encountered.
In contrast, statistical and connectionist approaches use the statistical or connectionist
model as guidance and build rules or data items automatically, usually in relatively large
quantity. After building rules or data items, all approaches then automatically apply them
to specific tasks in the system. For instance, connectionist approaches may apply the
rules to train the weights of links between units.
Research using these different approaches follows a general set of steps, System aspects:
from data analysis, rules, and basis for evaluation.
By system aspects, we mean source of data, theory or model formed - Data:
are usually not directly observable. Statistical and connectionist approaches are built on
the basis of machine observable facets of data, usually from text corpora.
As mentioned earlier, symbolic approaches use human introspective data, which - Theory or model based on data analysis:
formed for symbolic approaches whereas a parametric model is formed for statistical
approaches and a connectionist model is formed for connectionist approaches.
As the outcome of data analysis, a theory is - Rules:
detailed criteria of rule application. For statistical approaches, the criteria of rule
application are usually at the surface level or under-specified. For connectionist
approaches, individual rules typically cannot be recognized.
For symbolic approaches, the rule construction stage usually results in rules with - Basis for Evaluation:
judgments of unaffiliated subjects and may use system-internal measures of growth such
as the number of new rules. In contrast, the basis for evaluation of statistical and
connectionist systems are usually in the form of scores computed from some evaluation
function. However, if all approaches are utilized for the same task, then the results of the
task can be evaluated both quantitatively and qualitatively and compared.
Evaluation of symbolic systems is typically based on intuitive Robustness:
input. To deal with anomalies, they can anticipate them by making the grammar more
general to accommodate them. Compared to symbolic systems, statistical systems may be
more robust in the face of unexpected input provided that training data is sufficient,
which may be difficult to be assured of. Connectionist systems may also be robust and
fault tolerant because knowledge in such systems is stored across the network. When
presented with noisy input, they degrade gradually.
Symbolic systems may be fragile when presented with unusual, or noisy Flexibility:
examples, symbolic systems may lack the flexibility to adapt dynamically to experience.
In contrast, statistical systems allow broad coverage, and may be better able to deal with
unrestricted text (21) for more effective handling of the task at hand. Connectionist
systems exhibit flexibility by dynamically acquiring appropriate behavior based on the
given input. For example, the weights of a connectionist network can be adapted in realtime
to improve performance. However, such systems may have difficulty with the
representation of structures needed to handle complex conceptual relationships, thus
limiting their abilities to handle high-level NLP (36).
Since symbolic models are built by human analysis of well-formulated Suitable tasks:
identifiable linguistic behavior. They can be used to model phenomena at all the various
linguistic levels described in earlier sections. Statistical approaches have proven to be
effective in modeling language phenomena based on frequent use of language as reflected
in text corpora. Linguistic phenomena that are not well understood or do not exhibit clear
regularity are candidates for statistical approaches. Similar to statistical approaches,
connectionist approaches can also deal with linguistic phenomena that are not well
understood. They are useful for low-level NLP tasks that are usually subtasks in a larger
problem.
To summarize, symbolic, statistical, and connectionist approaches have exhibited
different characteristics, thus some problems may be better tackled with one approach
while other problems by another. In some cases, for some specific tasks, one approach
may prove adequate, while in other cases, the tasks can get so complex that it might not
be possible to choose a single best approach. In addition, as Klavans and Resnik (6)
pointed out, there is no such thing as a “purely statistical” method. Every use of statistics
is based upon a symbolic model and statistics alone is not adequate for NLP. Toward this
end, statistical approaches are not at odds with symbolic approaches. In fact, they are
rather complementary. As a result, researchers have begun developing hybrid techniques
that utilize the strengths of each approach in an attempt to address NLP problems more
effectively and in a more flexible manner.
Symbolic approaches seem to be suited for phenomena that exhibit NATURAL LANGUAGE PROCESSING APPLICATIONS
Natural language processing provides both theory and implementations for a range of
applications. In fact, any application that utilizes text is a candidate for NLP. The most
frequent applications utilizing NLP include the following:
surprising that so few implementations utilize NLP. Recently, statistical approaches
for accomplishing NLP have seen more utilization, but few systems other than those
by Liddy (37) and Strzalkowski (38) have developed significant systems based on
NLP
Information Retrieval – given the significant presence of text in this application, it is recognition, tagging, and extraction into a structured representation, certain key
elements of information, e.g. persons, companies, locations, organizations, from large
collections of text. These extractions can then be utilized for a range of applications
including question-answering, visualization, and data mining.
Information Extraction (IE) – a more recent application area, IE focuses on the •
potentially relevant documents in response to a user’s query, question-answering
provides the user with either just the text of the answer itself or answer-providing
passages.
Question-Answering – in contrast to Information Retrieval, which provides a list of •
empower an implementation that reduces a larger text into a shorter, yet richlyconstituted
abbreviated narrative representation of the original document.
Summarization – the higher levels of NLP, particularly the discourse level, can •
NLP have been utilized in MT systems, ranging from the ‘word-based’ approach to
applications that include higher levels of analysis.
Machine Translation – perhaps the oldest of all NLP applications, various levels of •
envisioned by large providers of end-user applications. Dialogue systems, which
usually focus on a narrowly defined application (e.g. your refrigerator or home sound
system), currently utilize the phonetic and lexical levels of language. It is believed
that utilization of all the levels of language processing explained above offer the
potential for truly habitable dialogue systems.
CONCLUSIONS
While NLP is a relatively recent area of research and application, as compared to other
information technology approaches, there have been sufficient successes to date that
suggest that NLP-based information access technologies will continue to be a major area
of research and development in information systems now and far into the future.
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