Current Challenges in NLP Scope and opportunities

Current Challenges in NLP : Scope and opportunities

Things are getting smarter with NLP ( Natural Language Processing ) . Yesterday I met my friend who is using chatbot for mobile recharge . He said, ” I  feel its a magic to play around such features” . I am also a big fan for chatbot Technology . If you look at whats going on IT sectors ,you will see ,”Suddenly the IT Industry is taking a sharp turn where machine are more human like “. All this fun is just because of Implementation of  deep learning into NLP . NLP seems a complete suits of rocking features like Machine Translation , Voice Detection , Sentiment Extractions . It seems that most of things are finish and nothing to do more with NLP . Its a myth , there are still too many gaps . Gaps in the term of Accuracy , Reliability etc in existing NLP framworks  . These Gaps are Current Challenges in NLP .

This complete article will make a walk through Current Challenges in NLP : Scope and opportunities. If you can solve any of them , You are the next Hero  in Technical world . I am not joking , If you see most of the technologies Journals and conferences are full of research paper to improve the existing NLP algorithms and followed by framework and APIs.

Current Challenges in NLP : Scope and opportunities-

Current Challenges in NLP Scope and opportunities

See! if you are a Application developer , You know it very well ,”How much hard is to bring a latest research into your existing Application ” . Actually research is just a proof of concept .On the top of the POC , There are so many operations which you need to perform in Integration . Actually there is a complete life cycle for Integration of any latest research into Real Product or feature .

I have Identified current Application of NLP into three classes . First is Mature and already solved . Second is mature enough to integrate with third party applications with little modification . Third is still in research and if you are looking to build on the top of it , its going to be a challenge for you .Lets go for Current Challenges in NLP –

1.NLP APPLICATIONS ( Already Solved )-

Have you seen your spam box in Gmail . This also work on machine learning and NLP principal . This spam detection is too much accurate and reliable . Not only Gmail but most of the email space and service  provider are  providing the feature of spam email detector and auto filter  . Here is the list of such NLP features  –

  1. Part of Speech Tagging –

POS tagging is one the common task which most of the NLP frameworks and API provide .This helps in identifying the Part of Speech into sentences . Usually you will not get any end application of this NLP feature but it is one of the most required tool in the mid of other big NLP process ( Pipeline) .

     2. Name Entity Extraction –

you can use NLP to identify name of person , organization etc in a sentences . All you need to correctly implement the API in your application . It will automatically prompt the type of each word if its any Location , organization , person name etc . Now you must be thinking where  can we use this  Name entity recognizer  [NER]parser .

Suppose you are developing any App witch crawl any web page and extracting  some information about any company . Now the thing is after scrapping you have only the text . If you have to make any relation between this text with location . How can easily achieve with some fuzzy logic and NER parser . when you parse the sentence from the NER Parser it will prompt some Location . All you need to catch or store and proceed the flow of your app .

If I talk about its accuracy , Its highly accurate and reliable in almost every NLP   framework .  personally I have used

Stanford NLP and NLTK , I found its working as expected .

2.NLP APPLICATIONS ( Intermediate but reliable  ) –

Here is the list for  Current Challenges in NLP but mature enough –

2.1 – Sentiment Extraction –

In simple word the Sentiment Extraction is ,”Classifying sentence into their emotions” . I mean positive negative and neutral. Its not mandatory that you  put only these three classes but you can add few more classes like extra positive and extra negative etc .   sentiment extraction is quite accurate using current NLP frameworks and APIs.Still the research going on at sentiment extraction .

You can build very powerful application on the top of Sentiment Extraction feature . For example – if any companies wants to take the user review of it existing product . Company needs to pay good amount of money to market research firm . They start a survey and fill up the questionnaire  . Once all the data about user preference is collected . They do some analytics on the top of user preference data . At the end they create a report .

All these manual work is performed because we have to convert unstructured data to structured one . So how to automate it ? The answer is pretty simple directly process the unstructured the data . Sentiment extraction is one of that efforts  . using the sentiment extraction technique companies can import all user reviews and machine can extract the sentiment on the top of it .  its really cost effective and time saver .

2.2- Machine Translation –

You must have played around the Google Translate , If not first go and play with Google Translate .It can translate the text from one language to another . Actually the overall translation functionality is built on very complex computation on very complex data set .This complex data set is called corpus.

As it is already in market . So I will not speak that much about it . I will just say improving the accuracy in fraction is a real challenge now .  People are doing Phd in machine translation , some of them are working for improving the algorithms behind the translation and some of them are working to improve and enlarge the training data set ( Corpus ).

2.3 Information Extraction and Mapping –

When we read any sentence , our mind first understand the context of that . Once the context is understood , The second task is too grapse the entity of required information out of that .For example –

If I said – “I am traveling to India”.

Here machine should understand that –

context – traveling

Location- India , User – current logged in user in chat .

In the current time , we are able to fetch the information from such simpler or little harder to it . So this NLP feature is also mature enough .The scope in this area to resolve the complex sentences like –

If some body type or said – “Tomorrow is off”

here the office word is hidden . It is really hard for machine to fetch this  hidden information . Researchers are proposing some solution for it like tract the older conversation and all .  Its not the only challenge there are so many others .So if you are Interested in this filed , Go and taste the water of Information extraction in NLP .

2.4 –Coreference resolution-

We use pronouns like He , she , it etc . For human like us , It is really easy to map with the entities . In NLP when we write the algorithm behind the scene . It get confuse some , Although there are lot of proposed solution which are effective . Still lot of work is required . Lets understand with some example –

Rahul  said to sukesh that he will go .

Now resolving the association of word ( Pronoun) ‘he’ with Rahul and sukesh could be a challenge not necessarily . Its just an example to make you understand .What are current NLP challenge in Coreference resolution.

2.5 –Word sense disambiguation

In the continuation of current NLP challenges , word sense disambiguation is one the big area . Lets take an example -There is a sentence – ” Apple is good ”

Problem -How will you confirm that Apple as company or fruit ?

Implementation of Deep learning into NLP has solved most of such issue very accurately . Not only word sense disambiguation but neural networks are very useful in making decision on the previous conversation .

3. NLP APPLICATIONS ( Harder and In progress )-

Apart from the above , there are some area where NLP is little immature and under progress like –

3.1-Text Summarization

Most of the time we avoid reading larger content . We try to get some highlights or summary . The problem is writing the summary of a larger content manually is itself time taking process . To automate this process , AI for auto Summarization came into picture .

There are lot of API for Text Summarization but still in progress . This field is quite volatile and one of the hardest current challenge in  NLP .

3.2 Auto Dialogue Management –

There are so many chatbot framework and API . Most of them are cloud hosted like Google DialogueFlow .It is very easy to build a chatbot for demo . You will see in there are too many videos on youtube which claims to teach you chat bot development in 1 hours or less . The problem is about their reliability.

Chat bot are transforming the complete service Industry . Its market disrupting technology for this era . Actually it covers all NLP stuff which we cover so far . Now you can guess if there is a gap in any of the them it will effect the performance overall in chatbots .

How to start overcoming current  Challenges in NLP –

Always do some literature survey on blogs . Once you have basic understanding go for some good research paper on reputed journals like IEEE etc .Read them  and make your own foundation in NLP . Make sure you are reading the latest research paper . Now you have two choices –

  1. Either continue with paper and finish the future work mention by the author .You will find this section in most of the research paper at the end .
  2. Or you can replace the current work with your own approach and make a better version of it .You may have more than one base paper.
  3. Once you have decided what you are going to perform ,perform the experiment and check the results . Make a bench mark from where you compare the results on your approach .
  4. Do not forget to write a research paper if you have something  to contribute .

Conclusion on Current Challenges in NLP-

NLP is a good field to start research .There are so many component which are already built but not reliable . As you have seen ,this is the current snapshot for NLP challenges ,Still companies like Google and Apple etc are making their own efforts  .They are solving the problems and providing the solutions like  Google virtual Assistant etc .

I thinks that enough for today’s discussion on Current Challenges in NLP . How did you find this article , Please comment below.

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