In this article we will talk about natural language processing (NLP).If we don't know what is natural language processing then this article gonna help you lot.
Language is a method of
communication with the help of which we can speak, read and write. For example,
we think, we make decisions, plans and more in natural language; precisely, in
words. However, the big question that confronts us in this AI era is that can
we communicate in a similar manner with computers. In other words, can human
beings communicate with computers in their natural language? It is a challenge
for us to develop NLP applications because computers need structured data, but
human speech is unstructured and often ambiguous in nature.
In this sense, we can
say that Natural Language Processing (NLP) is the sub-field of Computer Science
especially Artificial Intelligence (AI) that is concerned about enabling
computers to understand and process human language. Technically, the main task
of NLP would be to program computers for analyzing and processing huge amount of
natural language data.
History of NLP-
We have divided the
history of NLP into four phases. The phases have distinctive concerns and
styles.
First Phase (Machine Translation
Phase) - Late 1940s to late 1960s
The work done in this
phase focused mainly on machine translation (MT). This phase was a period of
enthusiasm and optimism.
Let us now see all that
the first phase had in it −
·
The research on NLP started in early 1950s after Booth &
Richens’ investigation and Weaver’s memorandum on machine translation in 1949.
·
1954 was the year when a limited experiment on automatic
translation from Russian to English demonstrated in the Georgetown-IBM
experiment.
·
In the same year, the publication of the journal MT (Machine
Translation) started.
·
The first international conference on Machine Translation (MT) was
held in 1952 and second was held in 1956.
·
In 1961, the work presented in Teddington International Conference
on Machine Translation of Languages and Applied Language analysis was the high
point of this phase.
Second Phase (AI Influenced Phase) –
Late 1960s to late 1970s
In this phase, the work
done was majorly related to world knowledge and on its role in the construction
and manipulation of meaning representations. That is why, this phase is also
called AI-flavored phase.
The phase had in it, the
following −
·
In early 1961, the work began on the problems of addressing and
constructing data or knowledge base. This work was influenced by AI.
·
In the same year, a BASEBALL question-answering system was also
developed. The input to this system was restricted and the language processing
involved was a simple one.
·
A much advanced system was described in Minsky (1968). This
system, when compared to the BASEBALL question-answering system, was recognized
and provided for the need of inference on the knowledge base in interpreting
and responding to language input.
Third Phase (Grammatico-logical
Phase) – Late 1970s to late 1980s
This phase can be
described as the grammatico-logical phase. Due to the failure of practical
system building in last phase, the researchers moved towards the use of logic
for knowledge representation and reasoning in AI.
The third phase had the
following in it −
·
The grammatico-logical approach, towards the end of decade, helped
us with powerful general-purpose sentence processors like SRI’s Core Language
Engine and Discourse Representation Theory, which offered a means of tackling
more extended discourse.
·
In this phase we got some practical resources & tools like
parsers, e.g. Alvey Natural Language Tools along with more operational and
commercial systems, e.g. for database query.
·
The work on lexicon in 1980s also pointed in the direction of
grammatico-logical approach.
Fourth Phase (Lexical & Corpus
Phase) – The 1990s
We can describe this as
a lexical & corpus phase. The phase had a lexicalized approach to grammar
that appeared in late 1980s and became an increasing influence. There was a
revolution in natural language processing in this decade with the introduction
of machine learning algorithms for language processing.
Study of Human Languages
Language is a crucial
component for human lives and also the most fundamental aspect of our behavior.
We can experience it in mainly two forms - written and spoken. In the written
form, it is a way to pass our knowledge from one generation to the next. In the
spoken form, it is the primary medium for human beings to coordinate with each
other in their day-to-day behavior. Language is studied in various academic
disciplines. Each discipline comes with its own set of problems and a set of
solution to address those.
Ambiguity and Uncertainty in Language
Ambiguity, generally
used in natural language processing, can be referred as the ability of being
understood in more than one way. In simple terms, we can say that ambiguity is
the capability of being understood in more than one way. Natural language is
very ambiguous. NLP has the following types of ambiguities −
Lexical
Ambiguity
The ambiguity of a
single word is called lexical ambiguity. For example, treating the word silver as a noun, an adjective,
or a verb.
Syntactic
Ambiguity
This kind of ambiguity
occurs when a sentence is parsed in different ways. For example, the sentence
“The man saw the girl with the telescope”. It is ambiguous whether the man saw
the girl carrying a telescope or he saw her through his telescope.
Semantic
Ambiguity
This kind of ambiguity
occurs when the meaning of the words themselves can be misinterpreted. In other
words, semantic ambiguity happens when a sentence contains an ambiguous word or
phrase. For example, the sentence “The car hit the pole while it was moving” is
having semantic ambiguity because the interpretations can be “The car, while
moving, hit the pole” and “The car hit the pole while the pole was moving”.
Anaphoric
Ambiguity
This kind of ambiguity
arises due to the use of anaphora entities in discourse. For example, the horse
ran up the hill. It was very steep. It soon got tired. Here, the anaphoric
reference of “it” in two situations cause ambiguity.
Pragmatic
ambiguity
Such kind of ambiguity
refers to the situation where the context of a phrase gives it multiple
interpretations. In simple words, we can say that pragmatic ambiguity arises
when the statement is not specific. For example, the sentence “I like you too”
can have multiple interpretations like I like you (just like you like me), I
like you (just like someone else dose).
NLP Phases
Following diagram shows
the phases or logical steps in natural language processing −
Morphological
Processing
It is the first phase of
NLP. The purpose of this phase is to break chunks of language input into sets
of tokens corresponding to paragraphs, sentences and words. For example, a word
like “uneasy” can be
broken into two sub-word tokens as “un-easy”.
Syntax
Analysis
It is the second phase
of NLP. The purpose of this phase is two folds: to check that a sentence is
well formed or not and to break it up into a structure that shows the syntactic
relationships between the different words. For example, the sentence like “The school goes to the boy” would
be rejected by syntax analyzer or parser.
Semantic
Analysis
It is the third phase of
NLP. The purpose of this phase is to draw exact meaning, or you can say
dictionary meaning from the text. The text is checked for meaningfulness. For
example, semantic analyzer would reject a sentence like “Hot ice-cream”.
Pragmatic
Analysis
It is the fourth phase
of NLP. Pragmatic analysis simply fits the actual objects/events, which exist in
a given context with object references obtained during the last phase (semantic
analysis). For example, the sentence “Put the banana in the basket on the
shelf” can have two semantic interpretations and pragmatic analyzer will choose
between these two possibilities.
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