Natural Language Processing: State of The Art, Current Trends and

Challenges
Diksha Khurana1, Aditya Koli1, Kiran Khatter1,2 and Sukhdev Singh1,2
1
Manav Rachna University, India
2
Accendere Knowledge Management Services Pvt. Ltd., India
Abstract
Current paradigm of information technology uses human-computer interaction in the form of
natural language—the language we use for day-to-day communication. Natural Language
Processing (NLP) has recently gained much attention for representing and analysing human
language computationally. It has spread its applications in various fields such as machine
translation, email spam detection, information extraction, summarization, medical, and
question answering etc. The paper distinguishes various phases by discussing different levels
of NLP and components of Natural Language Generation (NLG) followed by presenting the
history and evolution of NLP, state of the art presenting the various applications of NLP and
current trends and challenges.
Keywords: Natural Language Processing, Human Computer Interaction, Natural Language
Generation

1. Introduction
Natural Language Processing (NLP) is a tract of Artificial Intelligence and Linguistics,
devoted to make computers understand the statements or words written in human languages.
Natural language processing came into existence to ease the user’s work and to satisfy the
wish to communicate with the computer in natural language. Since all the users may not be
well-versed in machine specific language, NLP caters those users who do not have enough
time to learn new languages or get perfection in it.

A language can be defined as a set of rules or a set of symbols. Symbols are combined and
used for conveying information or broadcasting the information. Symbols are tyrannized by
the rules. Natural Language Processing basically can be classified into two parts i.e. Natural
Language Understanding and Natural Language Generation which evolves the task to
understand and generate the text (Figure 1).

Figure 1. Broad Classification of NLP

Linguistics is the science of language which includes Phonology that refers to sound,
Morphology that refers to word formation, Syntax referring to sentence structure, Semantics
referring to syntax and Pragmatics which refers to understanding.

Noah Chomsky, one of the first linguists of the twelfth century who started syntactic theories,
marked a unique position in the field of theoretical linguistics because he revolutionized the
area of syntax1 which can be broadly categorized into two levels: Higher Level which
includes speech recognition and Lower Level which corresponds to natural language. Few of
the researched tasks of NLP are Automatic Summarization, Co-Reference Resolution,
Discourse Analysis, Machine Translation, Morphological Segmentation, Named Entity
Recognition, Optical Character Recognition, Part Of Speech Tagging etc. Some of these tasks
have direct real world applications such as Machine translation, Named entity recognition,
Optical character recognition etc. Automatic summarization produces an understandable
summary of a set of text and provides summaries or detailed information of text, of a known
type. Co-reference resolution refers to a sentence or a large set of text that determines which
words refer to the same object. Discourse analysis refers to the task of identifying the
discourse structure of connected text. Machine translation refers to the automatic translation
of text from one human language to another. Morphological segmentation refers to separating
a word into individual morphemes and identify the class of the morphemes. Named Entity
Recognition (NER) describes a stream of text, determines which items in the text relate to
proper names. Optical Character Recognition (OCR) gives an image representing printed
text, which helps in determining the corresponding or related text. Part of speech tagging
describes a sentence, determines the part of speech for each word. Though NLP tasks are
obviously very closely interweaved but they are used frequently,for convenience .Some of the
tasks such as automatic summarisation, co-reference analysis etc. act as subtasks that are used
in solving larger tasks.

The goal of Natural Language Processing is to accommodate one or more specialities of an

algorithm or system.The metric of NLP assess on an algorithmic system that allows for the
integration of language understanding and language generation. It is even used in
multilingual event detection2. A novel modular system is proposed for cross-lingual event
extraction for English, Dutch and Italian texts by using different pipelines for different
languages. The system incorporates a modular set of foremost multilingual Natural Language
Processing (NLP) tools. The pipeline integrates modules for basic NLP processing as well as
for more advanced tasks such as cross-lingual named entity linking, semantic role labelling
and time normalization. Thus, the cross-lingual framework allows for the interpretation of
events, participants, locations and time, as well as the relations between them. Output of these
individual pipelines is intended to be used as input for a system that obtains event centric
knowledge graphs. All modules behave like UNIX pipes: they all take standard input, to do
some annotation, and produce standard output which in turn is the input for the next module.
Pipelines are built as a data centric architecture so that modules can be adapted and replaced.
Furthermore, modular architecture allows the different configurations and dynamic
distribution.
Most of the work in Natural Language Processing is conducted by computer scientists while
various other professionals like linguistics, psychologist and philosophers etc. have also
shown interest. One of the most ironical aspects of NLP is that it adds up to the knowledge of
human language. The field of Natural Language Processing is related with different theories
and techniques that deal with the problem of natural language of communicating with the
computers. Ambiguity is one of the major problems of natural language which is usually
faced in syntactic level with further subtasks like lexical and morphological concerned with
the study of words and word formation. Each of these levels can produce ambiguities that can
be solved by the knowledge of the complete sentence. The ambiguity can be solved by
various methods such as Minimizing Ambiguity, Preserving Ambiguity, Interactive
Disambiguity and Weighting Ambiguity3. Some of the methods proposed by researchers to
remove ambiguity is by preserving ambiguity3,4,5. The objectives are closely in line with the
last of these: it cover a wide range of ambiguities and there is a statistical element implicit in
their approach.
2. Levels of NLP
The ‘levels of language’ are one of the most explanatory methods for representing the Natural
Language Processing which helps to generate the NLP text by realizing Content Planning,
Sentence Planning and Surface Realization. (Figure 2)

Figure 2. Phases of NLP architecture

Linguistic is the science which involves meaning of language, language context and various
forms of language. The various important terminologies of Natural Language Processing are:-

1. Phonology
Phonology is the part of linguistics which refers to the systematic arrangement of sound. The
term phonology comes from Ancient Greek and the term phono- means voice or sound, and
the suffix –logy refers to word or speech. In 1993 Nikolai Trubetzkoy stated that Phonology
that is “the study of sound pertaining to the system of language,whereas Lass in 1998 wrote
that phonology refers broadly with the sounds of language, concerned with the lathe sub
discipline of linguistics, it could be better explained as, "phonology proper is concerned with
the function, behaviour and organization of sounds as linguistic items. It includes semantic
use of sound to encode meaning of any human language 6.
2. Morphology
The different part of the word represent the smallest units of meaning known as Morphemes.
Morphology which comprises of nature of words, are initiated by morphemes. An example of
Morpheme could be, the word precancellation which can be morphologically scrutinized into
three separate morphemes: the prefix pre, the root cancella, and the suffix -tion. The
interpretation of morpheme stays same across all the words and just to understand the
meaning humans can break any unknown word into morphemes. For example, adding the
suffix –ed to a verb, conveys that the action of the verb took place in the past. The words that
cannot be divided and have meaning by themselves are called Lexical morphemes (e.g.: table,
chair).The words (e.g. -ed, -ing, -est, -ly, -ful) that are combined with the lexical morpheme
are known as Grammatical morphemes (eg. Worked, Consulting, Smallest, Likely, Use).
Those grammatical morphemes that occur in combination called bound morphemes (eg. -ed, -
ing). Grammatical morphemes can be further divided into bound morphemes and derivational
morphemes.
3. Lexical
In Lexical, humans as well as NLP systems can interpret the meaning of individual words.
Several types of processing bestow to word-level understanding – the first of these being a
part-of-speech tag to each word. In this processing, words that can act as more than one part-
of-speech which are assigned to the most probable part-of speech tag based on the context in
which they occur. At the lexical level, semantic representations can be replaced by the words
that have one meaning. In NLP system, the nature of the representation varies according to
the semantic theory deployed.
4. Syntactic
This level emphasis to examine the words in a sentence so as to uncover the grammatical
structure of the sentence. Both grammar and parser are required in this level. The output of
this level of processing is the representation of the sentence that communicate the structural
dependency relationships between the words.There are various grammars that can be
hindered. Not all NLP applications require a full parse of sentences. They even face a lot of
challenges in parsing of prepositional phrase attachment and conjunction audit no longer
impede that plea for which phrasal and clausal dependencies are adequate 7.The syntax
conveys meaning in most languages because order and dependency contribute to connotation.
For example, the two sentences: ‘The cat chased the mouse’ and ‘The mouse chased the cat’
differ only in terms of syntax, yet convey quite different meaning.
5. Semantic
In semantic, most people think that meaning is determined, however this is not it is all the
levels that bestow to meaning. Semantic processing determines the possible meanings of a
sentence by pivoting on the interactions among word-level meanings in the sentence. This
level of processing can incorporate the semantic disambiguation of words with multiple
senses; in a cognate way to how syntactic disambiguation of words that can work as multiple
parts-of-speech is handy at the syntactic level. For example, amongst other meanings, ‘file’ as
a noun can mean either a binder for gathering papers, or a tool to form one’s fingernails, or a
line of individuals in a queue 7. The semantic level survey words for their dictionary report,
but also for the report they derive from the context of the sentence. Semantics context that
most words have more than one report but that we can spot the appropriate one by looking at
the rest of the sentence8 .
6. Discourse
While syntax and semantics travail with sentence-length units, the discourse level of NLP
travail with units of text longer than a sentence i.e, it does not interpret multi sentence texts as
just sequence sentences, a piece of which can be reported singly. Rather, discourse focuses on
the properties of the text as a whole that convey meaning by making connections between
component sentences7. The two of the most common levels are Anaphora Resolution -It is the
replacing of words such as pronouns, which are semantically stranded, with the pertinent
entity to which they refer. Discourse/Text Structure Recognition - Discourse/text structure
recognition sway the functions of sentences in the text, which in turn adds to the meaningful
representation of the text.
7. Pragmatic:
Pragmatic is concerned with the firm use of language in situations and utilizes nub over and
above the nub of the text for understanding the goal and to explain how extra meaning is read
into texts without literally being encoded in them. This requisite much world knowledge,
including the understanding of intentions, plans, and goals. For example, the following two
sentences need aspiration of the anaphoric term ‘they’, but this aspiration requires pragmatic
or world knowledge7.
3. Natural Language Generation
Natural Language Generation (NLG) is the process of producing phrases, sentences and
paragraphs that are meaningful from an internal representation. It is a part of Natural
Language Processing and happens in four phases: identifying the goals, planning on how
goals maybe achieved by evaluating the situation and available communicative sources and
realizing the plans as a text as shown in Figure 3. It is opposite to Understanding.

Figure 3. Components of NLG

Components of NLG are as follows:

Speaker and Generator – To generate the text we need to have a speaker or an application
and a generator or a program that renders the application’s intentions into fluent phrase
relevant to the situation.

Components and Levels of Representation -The process of language generation involves

the following interweaved tasks. Content selection: Information should be selected and
included in the set. Depending on how this information is parsed into representational units,
parts of the units may have to be removed while some others may be added by default.
Textual Organization: The information must be textually organized according the grammar, it
must be ordered both sequentially and in terms of linguistic relations like modifications.
Linguistic Resources: To support the information’s realization, linguistic resources must be
chosen. In the end these resources will come down to choices of particular words, idioms,
syntactic constructs etc. Realization: The selected and organized resources must be realized
as an actual text or voice output.

Application or Speaker – This is only for maintaining the model of the situation. Here the
speaker just initiates the process doesn’t take part in the language generation. It stores the
history, structures the content that is potentially relevant and deploys a representation of what
is actually known. All these form the situation, while selecting subset of propositions that
speaker has. The only requirement is the speaker that has to make a sense of the situation9 .

4. History of NLP
In late 1940s the term wasn’t even in existence, but the work regarding machine translation
(MT) had started. Research in this period was not completely localized. Russian and English
were the dominant languages for MT, but others, like Chinese were used for MT
(Booth ,1967)10. MT/NLP research almost died in 1966 according to ALPAC report, which
concluded that MT is going now here. But later on some MT production systems were
providing output to their customers11. By this time, they started working on the use of
computers for literary and linguistic studies had also started.
As early as 1960 signature work influenced by AI began, with the BASEBALL Q-A
systems12. LUNAR13 and Winograd SHRDLU were natural successors of these systems but
they were seen as stepped up sophistication, in terms of their linguistic and their task
processing capabilities. There was a widespread belief that progress could only be made on
the two sides, one is ARPA Speech Understanding Research (SUR) project and other in some
major system developments projects building database front ends. The front-end projects 14
were intended to go beyond LUNAR in interfacing the large databases.
In early 1980s computational grammar theory became a very active area of research linked
with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions
and with functions like emphasis and themes.
By the end of the decade the powerful general purpose sentence processors like SRI’s Core
Language Engine15 and Discourse Representation Theory16 offered a means of tackling more
extended discourse within the grammatico-logical framework. This period was one of the
growing community. Practical resources, grammars, and tools and parsers became available
e.g the Alvey Natural Language Tools 17.The (D)ARPA speech recognition and message
understanding information extraction conferences were not only for the tasks they addressed
but for the emphasis on heavy evaluation, thus starting a trend that became a major feature in
the 1990s18,19. They work on user modelling 20 that was one strand in research paper and on
discourse structure serving21. At the same time, as McKeown22 showed, rhetorical schemas
which could be used for producing both linguistically coherent and communicatively
effective text. Some researches in NLP marked important topics for future like word sense
disambiguation23and probabilistic networks, statistically coloured NLP, the work on the
lexicon, also pointed in this direction.
Statistical language processing was a major thing in 90s 24, because this not only involves data
analysts. Information extraction and automatic summarising 25 was also a point of focus.
Recent researches are mainly focused on unsupervised and semi-supervised learning
algorithms.
5. Related Work
Many researchers worked on NLP, building tools and systems which makes NLP what it is
today. Tools like Sentiment Analyser, Parts of Speech (POS)Taggers, Chunking, Named
Entity Recognitions (NER), Emotion detection, Semantic Role Labelling made NLP a good
topic for research.
Sentiment analyser26 works by extracting sentiments about given topic. Sentiment analysis
consists of a topic specific feature term extraction, sentiment extraction, and association by
relationship analysis. Sentiment Analysis utilizes two linguistic resources for the analysis:
The sentiment lexicon and the sentiment pattern database. It analyses the documents for
positive and negative words and try to give ratings on scale -5 to +5.
Parts of speech taggers for the languages like European languages, research is being done on
making parts of speech taggers for other languages like Arabic, Sanskrit 27, Hindi28 etc. It can
efficiently tag and classify words as nouns, adjectives, verbs etc. The most procedures for
part of speech can work efficiently on European languages, but it won’t on Asian languages
or middle eastern languages. Sanskrit part of speech tagger is specifically uses treebank
technique. Arabic uses Support Vector Machine (SVM)29 approach to automatically tokenize,
parts of speech tag and annotate base phrases in Arabic text.

Chunking – it is also known as Shadow Parsing; it works by labelling segments of sentences

with syntactic correlated keywords like Noun Phrase and Verb Phrase (NP or VP). Every
word has a unique tag often marked as Begin Chunk (B-NP) tag or Inside Chunk (I-NP) tag.
Chunking is often evaluated using the CoNLL 2000 shared task. CoNLL 2000 provides test
data for Chunking. Since then, a certain number of systems arised 30,31,32 all reporting around
94.3% F1 score. These systems use features composed of words, POS tags, and tags.

Usage of Named Entity Recognition in places such as Internet is a problem as people don’t
use traditional or standard English. This degrades the performance of standard natural
language processing tools substantially. By annotating the phrases or tweets and building
tools trained on unlabelled, in domain and out domain data 33. It improves the performance as
compared to standard natural language processing tools.

Emotion Detection34 is similar to sentiment analysis, but it works on social media platforms
on mixing of two languages I.e English + Any other Indian Language. It categorizes
statements into six groups based on emotions. During this process, they were able to identify
the language of ambiguous words which were common in Hindi and English and tag lexical
category or parts of speech in mixed script by identifying the base language of the speaker.

Sematic Role Labelling – SRL works by giving a semantic role to a sentence. For example in
the PropBank35 formalism, one assigns roles to words that are arguments of a verb in the
sentence. The precise arguments depend on verb frame and if there exists multiple verbs in a
sentence, it might have multiple tags. State-of-the-art SRL systems comprise of several
stages: creating a parse tree, identifying which parse tree nodes represent the arguments of a
given verb, and finally classifying these nodes to compute the corresponding SRL tags.

Event discovery in social media feeds 36, using a graphical model to analyse any social media
feeds to determine whether it contains name of a person or name of a venue, place, time etc.
The model operates on noisy feeds of data to extract records of events by aggregating
multiple information across multiple messages, despite the noise of irrelevant noisy messages
and very irregular message language, this model was able to extract records with high
accuracy. However, there is some scope for improvement using broader array of features on
factors.

6. Applications of NLP
Natural Language Processing can be applied into various areas like Machine Translation,
Email Spam detection, Information Extraction, Summarization, Question Answering etc.

6.1 Machine Translation

As most of the world is online, the task of making data accessible and available to all is a
great challenge. Major challenge in making data accessible is the language barrier. There are
multitude of languages with different sentence structure and grammar. In this the phrases are
translated from one language to another with the help of a statistical engine like Google
Translate. The challenge with machine translation technologies is not directly translating
words but keeping the meaning of sentences intact along with grammar and tenses. The
statistical machine learning gathers as much data as they can and then find that seems to be
parallel between two languages and they crunch their data to find the likelihood that
something in Language A corresponds to something in Language B. As for Google, in
September 2016, announced a new machine translation system based on Artificial neural
networks and Deep learning . In recent years, various methods have been proposed to
automatically evaluate machine translation quality by comparing hypothesis translations with
reference translations. Examples of such methods are word error rate, position-independent
word error rate37, generation string accuracy38, multi-reference word error rate39, BLEU
score40, NIST score41 All these criteria try to approximate human assessment and often
achieve an astonishing degree of correlation to human subjective evaluation of fluency and
adequacy42,43.

6.2 Text Categorization

Categorization systems inputs a large flow of data like official documents, military casualty
reports, market data, newswires etc. and assign them to predefined categories or indices. For
example, The Carnegie Group’s Construe system44 , inputs Reuters articles and saves much
more time by doing the work that is to be done by staff or human indexers. Some companies
have been using categorization systems to categorize trouble tickets or complaint requests
and routing to the appropriate desks. Another application of text categorization is email spam
filters. Spam filters is becoming important as the first line of defense against the unwanted
emails. A false negative and false positive issues of spam filters are at the heart of NLP
technology, it brought down to the challenge of extracting meaning from strings of text. A
filtering solution that is applied to an email system uses a set of protocols to determine which
of the incoming messages are spam and which are not. There are several types of spam filters
available: Content filters: Review the content within the message to determine whether it is a
spam or not. Header filters: Review the email header looking for fake information. General
Blacklist filters: Stops all emails from blacklisted recipients. Rules Based Filters: It uses user-
defined criteria. Such as stopping mails from specific person or stopping mail including a
specific word. Permission Filters: Require anyone sending a message to be pre-approved by
the recipient. Challenge Response Filters: Requires anyone sending a message to enter a code
in order to gain permission to send email.

6.3 Spam Filtering

It works using text categorization and in recent times, various machine learning techniques
have been applied to text categorization or Anti-Spam Filtering like Rule Learning 45, Naïve
Bayes46,47,48, Memory based Learning47, Support vector machines49, Decision Trees50,
Maximum Entropy Model51 which Sometimes combine different learners52. Using these
approaches is better as classifier is learned from training data rather than making by hand.
The naïve bayes is preferred because of its performance despite its simplicity 53. In Text
Categorization two types of models have been used 54. Both modules assume that a fixed
vocabulary is present. But in first model a document is generated by first choosing a subset of
vocabulary and then using the selected words any number of times, at least once irrespective
of order. This is called Multi-variate Bernoulli model. It takes the information of which
words are used in a document irrespective of number of words and order. In second model, a
document is generated by choosing a set of word occurrences and arranging them in any
order. this model is called multi-nomial model, in addition to the Multi-variate Bernoulli
model, it also captures information on how many times a word is used in a document. Most
text categorization approaches to anti-spam Email filtering have used multi-variate Bernoulli
model47.

6.4 Information Extraction

Information extraction is concerned with identifying phrases of interest of textual data. For
many applications, extracting entities such as names, places, events, dates, times and prices is
a powerful way of summarize the information relevant to a user’s needs. In the case of a
domain specific search engine, the automatic identification of important information can
increase accuracy and efficiency of a directed search. There is use of hidden Markov models
(HMMs) to extract the relevant fields of research papers. These extracted text segments are
used to allow searched over specific fields and to provide effective presentation of search
results and to match references to papers. For example, noticing the pop up ads on any
websites showing the recent items you might have looked on an online store with discounts.
In Information Retrieval two types of models have been used 55. Both modules assume that a
fixed vocabulary is present. But in first model a document is generated by first choosing a
subset of vocabulary and then using the selected words any number of times, at least once
without any order. This is called Multi-variate Bernoulli model. It takes the information of
which words are used in a document irrespective of number of words and order. In second
model, a document is generated by choosing a set of word occurrences and arranging them in
any order. this model is called multi-nomial model, in addition to the Multi-variate Bernoulli
model , it also captures information on how many times a word is used in a document

Discovery of knowledge is becoming important areas of research over the recent years.
Knowledge discovery research use a variety of techniques in order to extract useful
information from source documents like

Parts of Speech (POS) tagging, Chunking or Shadow Parsing, Stop-words I.e Keywords that
are used and must be removed before processing documents, Stemming I.e Mapping words to
some base for, it has two methods, dictionary based stemming and Porter style stemming 55.
Former one has higher accuracy but higher cost of implementation while latter has lower
implementation cost and is usually insufficient for IR. Compound or Statistical Phrases
Compounds and statistical phrases index multi token units instead of single tokens. Word
Sense Disambiguation refers to Word sense disambiguation is the task of understanding the
correct sense of a word in context. When used for information retrieval, terms are replaced by
their senses in the document vector.

Its extracted information can be applied on a variety of purpose, for example to prepare a
summary, to build databases, identify keywords, classifying text items according to some pre-
defined categories etc. For example CONSTRUE, it was developed for Reuters, that is used
in classifying news stories57. It has been suggested that many IE systems can successfully
extract terms from documents, acquiring relations between the terms is still a difficulty.
PROMETHEE is a system that extracts lexico-syntactic patterns relative to a specific
conceptual relation58. IE systems should work at many levels, from word recognition to
discourse analysis at the level of the complete document. An application of the Blank Slate
Language Processor (BSLP)59 approach for the analysis of a real life natural language corpus
that consists of responses to open-ended questionnaires in the field of advertising.
There’s a system called MITA (Metlife’s Intelligent Text Analyzer) 60 that extracts
information from life insurance applications. Ahonen et al. 61 suggested a mainstream
framework for text mining that uses pragmatic and discourse level analyses of text.
6.5 Summarization
Overload of information is the real thing in this digital age, and already our reach and access
to knowledge and information exceeds our capacity to understand it. This trend is not slowing
down, so an ability to summarize the data while keeping the meaning intact is highly
required. This is important not just allowing us the ability to recognize the understand the
important information for a large set of data, it is used to understand the deeper emotional
meanings; For example, a company determine the general sentiment on social media and use
it on their latest product offering. This application is useful as a valuable marketing asset.

The types of text summarization depends on the basis of the number of documents and the
two important categories are single document summarization and multi document
summarization62,63. Summaries can also be of two types: generic or query-focused 64,65,66,67.
Summarization task can be either supervised or unsupervised 68,63,69. Training data is required
in a supervised system for selecting relevant material from the documents. Large amount of
annotated data is needed for learning techniques. Few techniques are as follows–
- Bayesian Sentence based Topic Model (BSTM) uses both term-sentences and term
document associations for summarizing multiple documents70.
- Factorization with Given Bases (FGB) is a language model where sentence bases are
the given bases and it utilizes document-term and sentence term matrices. This
approach groups and summarizes the documents simultaneously71.
- Topic Aspect-Oriented Summarization (TAOS) is based on topic factors. These topic
factors are various features that describe topics such as capital words are used to
represent entity. Various topics can have various aspects and various preferences of
features are used to represent various aspects72.

6.6 Dialogue System

Perhaps the most desirable application of the future, in the systems envisioned by large
providers of end user applications is Dialogue systems, which focuses on a narrowly defined
applications (like refrigerator or home theater systems) currently uses the phonetic and lexical
levels of language. It is believed that these dialogue systems when utilizing all levels of
language processing offer potential for fully automated dialog systems 7. Whether on text or
via voice. This could lead to produce systems that can enable robots to interact with humans
in natural languages. Examples like Google’s assistant, Windows Cortana, Apple’s Siri and
Amazon’s Alexa are the software and devices that follow Dialogue systems.

6.7 Medicine
NLP is applied in medicine field as well. The Linguistic String Project-Medical Language
Processor is one the large scale projects of NLP in the field of medicine 74,75,76,77,78. The LSP-
MLP helps enabling physicians to extract and summarize information of any signs or
symptoms, drug dosage and response data with aim of identifying possible side effects of any
medicine while highlighting or flagging data items 74. The National Library of Medicine is
developing The Specialist System79,80,81,82,83. It is expected to function as Information
Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts. The lexicon
was created using MeSH (Medical Subject Headings), Dorland’s Illustrated Medical
Dictionary and general English Dictionaries. The Centre d’Informatique Hospitaliere of the
Hopital Cantonal de Geneve is working on an electronic archiving environment with NLP
features84,85. In first phase, patient records were archived . At later stage the LSP-MLP has
been adapted for French86,87,88,89 , and finally , a proper NLP system called RECIT 90,91,92,93 has
been developed using a method called Proximity Processing 94. It’s task was to implement a
robust and multilingual system able to analyze/comprehend medical sentences, and to
preserve a knowledge of free text into a language independent knowledge representation 95,96.
The Columbia university of New York has developed an NLP system called MEDLEE
(MEDical Language Extraction and Encoding System) that identifies clinical information in
narrative reports and transforms the textual information into structured representation 97.
 7. Approaches
Rationalist approach or symbolic approach assume that crucial part of the knowledge in the
human mind is not derived by the sense but is firm in advance, probably by genetic in
heritance. Noam Chomsky was the strongest advocate of this approach. It was trusted that
machine can be made to function like human brain by giving some fundamental knowledge
and reasoning mechanism linguistics knowledge is directly encoded in rule or other forms of
representation. This helps automatic process of natural languages. [98] Statistical and machine
learning entail evolution of algorithms that allow a program to infer patterns. An iterative
process is used to characterize a given algorithm’s underlying algorithm that are optimised by
a numerical measure that characterize numerical parameters and learning phase. Machine-
learning models can be predominantly categorized as either generative or discriminative.
Generative methods can generate synthetic data because of which they create rich models of
probability distributions. Discriminative methods are more functional and have right
estimating posterior probabilities and are based on observations.

Srihari99 explains the different generative models as one with a resemblance that is used to
spot an unknown speaker’s language and would bid the deep knowledge of numerous
language to perform the match. Whereas discriminative methods rely on a less knowledge-
intensive approach and using distinction between language. Whereas generative models, can
become troublesome when many features are used and discriminative models allow use of
more features100. Few of the examples of discriminative methods are Logistic regression and
conditional random fields (CRFs), generative methods are Naive Bayes classifiers and hidden
Markov models (HMMs).

7.1 Hidden Markov Model (HMM)

An HMM is a system where a shifting takes place between several states, generating feasible
output symbols with each switch. The sets of viable states and unique symbols may be large,
but finite and known. We can descry the outputs, but the system’s internals are hidden. Few
of the problem could be solved are by Inference A certain sequence of output symbols,
compute the probabilities of one or more candidate states with sequences. Pattern matching
the state-switch sequence is realised are most likely to have generated a particular output-
symbol sequence. Training the output-symbol chain data, reckon the state-switch/output
probabilities that fit this data best.

Hidden Markov Models are extensively used for speech recognition, where the output
sequence is matched to the sequence of individual phonemes. Frederick Jelinek, a statistical-
NLP advocate who first instigated HMMs at IBM’s Speech Recognition Group, reportedly
joked, every time a linguist leaves my group, the speech recognizer’s performance improves.
[101]
HMM is not restricted to this application it has several others such as bioinformatics
problems, for example, multiple sequence alignment102. Sonnhammer mentioned that Pfam
hold multiple alignments and hidden Markov model based profiles (HMM-profiles) of entire
protein domains. The cue of domain boundaries, family members and alignment is done
semi-automatically found on expert knowledge, sequence similarity, other protein family
databases and the capability of HMM-profiles to correctly identify and align the members 103.
 7.2 Naive Bayes Classifiers
The choice of area is wide ranging covering usual items like word segmentation and
translation but also unusual areas like segmentation for infant learning and identifying
documents for opinions and facts. In addition, exclusive article was selected for its use of
Bayesian methods to aid the research in designing algorithms for their investigation.

8. NLP in Talk
This section discusses the recent developments in the NLP projects implemented by various
companies and these are as follows:
8.1 ACE Powered GDPR Robot Launched by RAVN Systems 104
RAVN Systems, an leading expert in Artificial Intelligence (AI), Search and Knowledge
Management Solutions, announced the launch of a RAVN ("Applied Cognitive Engine") i.e
powered software Robot to help and facilitate the GDPR ("General Data Protection
Regulation") compliance.
The Robot uses AI techniques to automatically analyse documents and other types of data in
any business system which is subject to GDPR rules. It allows users to quickly and easily
search, retrieve, flag, classify and report on data mediated to be supersensitive under GDPR.
Users also have the ability to identify personal data from documents, view feeds on the latest
personal data that requires attention and provide reports on the data suggested to be deleted or
secured. RAVN's GDPR Robot is also able to hasten requests for information (Data Subject
Access Requests - "DSAR") in a simple and efficient way, removing the need for a physical
approach to these requests which tends to be very labour thorough. Peter Wallqvist, CSO at
RAVN Systems commented, "GDPR compliance is of universal paramountcy as it will
exploit to any organisation that control and process data concerning EU citizens.
LINK:http://markets.financialcontent.com/stocks/news/read/33888795/
RAVN_Systems_Launch_the_ACE_Powered_GDPR_Robot

8.2 Eno A Natural Language Chatbot Launched by Capital One105

Capital one announces chatbot for customers called Eno. Eno is a natural language chatbot
that people socialize through texting. Capital one claims that Eno is First natural language
SMS chatbot from a U.S. bank that allows customer to ask questions using natural language.
Customers can interact with Eno asking questions about their savings and others using a text
interface. Eno makes such an environment that it feels that a human is interacting. Ken
Dodelin, Capital One’s vice president of digital product development, said “We kind of
launched a chatbot and didn’t know it.”

This provides a different platform than other brands that launch chatbots like Facebook
Messenger and Skype. They believed that Facebook has too much access of private
information of a person, which could get them into trouble with privacy laws of U.S.
financial institutions work under. Like any Facebook Page admin can access full transcripts
of the bot’s conversations. If that would be the case then the admins could easily view the
personal banking information of customers with is not correct

LINK: https://www.macobserver.com/analysis/capital-one-natural-language-chatbot-eno/
 8.3 Future of BI in Natural Language Processing106
Several companies in BI spaces are trying to get with the trend and trying hard to ensure that
data becomes more friendly and is easily accessible. But still there is a long way for this.BI
will also make it easier to access as GUI is not needed. Because now a days the queries are
made by text or voice command on smartphones.one of the most common example is Google
might tell you today what will be the tomorrows weather. But soon enough, we will be able
to ask our personal data (chatbot) about customer sentiment today, and how do we feel about
their brand next week; all while walking down the street. Today, NLP tends to be based on
turning natural language into machine language. But with time the technology matures –
especially the AI component –the computer will get better at “understanding” the query and
start to deliver answers rather than search results.

Initially, the data chatbot will probably ask the question as how have revenues changed over
the last three-quarters?’ and then return pages of data for you to analyse. But once it learns
the semantic relations and inferences of the question, it will be able to automatically perform
the filtering and formulation necessary to provide an intelligible answer, rather than simply
showing you data.

Link: http://www.smartdatacollective.com/eran-levy/489410/here-s-why-natural-language-processing-future-bi

8.4 Using Natural Language Processing and Network Analysis to

Develop a Conceptual Framework for Medication Therapy
Management Research107
Natural Language Processing and Network Analysis to Develop a Conceptual Framework for
Medication Therapy Management Research describes a theory derivation process that is used
to develop conceptual framework for medication therapy management (MTM) research. The
MTM service model and chronic care model are selected as parent theories. Review article
abstracts target medication therapy management in chronic disease care that were retrieved
from Ovid Medline (2000-2016).

Unique concepts in each abstract are extracted using Meta Map and their pairwise
cooccurrence are determined. Then the information is used to construct a network graph of
concept co-occurrence that is further analysed to identify content for the new conceptual
model. 142 abstracts are analysed. Medication adherence is the most studied drug therapy
problem and co-occurred with concepts related to patient-centred interventions targeting self-
management. The enhanced model consists of 65 concepts clustered into 14 constructs. The
framework requires additional refinement and evaluation to determine its relevance and
applicability across a broad audience including underserved settings.

Link: https://www.ncbi.nlm.nih.gov/pubmed/28269895?dopt=Abstract

8.5 Meet the Pilot, world’s first language translating earbuds108

The world’s first smart earpiece Pilot will soon be transcribed over 15 languages. According
to Spring wise, Waverly Labs’ Pilot can already transliterate five spoken languages, English,
French, Italian, Portuguese and Spanish, and seven written affixed languages, German, Hindi,
Russian, Japanese, Arabic, Korean and Mandarin Chinese. The Pilot earpiece is connected
via Bluetooth to the Pilot speech translation app, which uses speech recognition, machine
translation and machine learning and speech synthesis technology.

Simultaneously, the user will hear the translated version of the speech on the second earpiece.
Moreover, it is not necessary that conversation would be taking place between two people
only the users can join in and discuss as a group. As if now the user may experience a few
second lag interpolated the speech and translation, which Waverly Labs pursue to reduce.
The Pilot earpiece will be available from September, but can be pre-ordered now for $249.
The earpieces can also be used for streaming music, answering voice calls and getting audio
notifications.

Link:https://www.indiegogo.com/projects/meet-the-pilot-smart-earpiece-language-translator-
headphones-travel#/

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