The presentation takes a look at Natural Language Processing, what it is, what problems it poses for new technology, how the likes of Google and Microsoft are tackling it and what effect the further development of natural language processing technique may have on the future of search and wearable technology.

1. Cloudspotting Presentation
Natural Language Processing
(NLP)
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2. What is Natural Language Processing?
Natural language processing (NLP) is the ability of computer programmes to understand
human speech, as it is actually spoken.
That means NLP has to tackle the often ambiguous and highly complex linguistic
structures people use in everyday speech. As such there are many variables these
computer programmes have to understand such as: slang, errors, regional dialect and
social context, in order to process language correctly and indeed, naturally.
Typical approaches to NLP are based on machine learning, which is a type of artificial
intelligence centred on identifying the uses and patterns in data.
Most of today’s NLP research revolves around search.
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http://searchcontentmanagement.techtarget.com/definition/natural-language-processing-NLP

3. What is Natural Language Processing?
Ultimately the task for NLP is to
eliminate the need for computer
programming languages such as
Java, PHP and ColdFusion.
Instead, if NLP is successful, we
will simply communicate with
machines in “human” languages.
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http://searchcontentmanagement.techtarget.com/definition/natural-language-processing-NLP
http://eil.stanford.edu/xpan/natural_language_processing_files/image002.gif (Image)

4. What are the Challenges of NLP?
•
Meeting the expectations of the user.
•
Understanding ambiguity in natural language.
•
Understanding the effect of context on meaning.
•
Understanding the referents of phrases like: he, she, and it. (Anaphoric Referencing)
•
Speed and efficiency of the interface.
•
Recognising relevant data, while disregarding the irrelevant data like age & gender.
© Cloudspotting 2013

5. What are the Challenges of NLP?
Generally speaking NLP has been successful in handling the challenges posed by the
syntax (structure) of natural language, but researchers and programmers still have a long
way to go before they meet the challenges posed by the semantics (sense and meaning)
of natural languages.
The main issues to solve are: understanding the meaning of a single word, understanding
the meaning of that word in connection with other words in the syntax and finally
understanding both those meanings in the context in which they are spoken.
Some of the these contexts in which utterances are spoken are: time, place, situation…
© Cloudspotting 2013
http://language.worldofcomputing.net/nlp-overview/open-problems-in-natural-langauge-processing.html

6. Meeting Expectations
Users expect to be able to converse with
machines in the same way they converse
with other human beings. That means not
having to change their accent, dialect or
even simply the volume of their speech in
order for machines to correctly process it.
(How many of us have found ourselves
over enunciating or shouting at Siri to try
and get through to it? This is an example of
when NLP does not meet our
expectations.)
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http://wpuploads.appadvice.com/wp-content/uploads/2013/09/140.jpg(Image)

7. Understanding Ambiguity & Context
Something is ambiguous when it can be understood or interpreted in two or more
possible ways and this can apply at the single word level or at the sentence level.
Humans are exceptionally good at resolving ambiguity in natural language due to our
understanding of context and knowledge of the world, however, computer systems do not
have this knowledge and understanding and so ambiguity and context pose a great many
problems for computers trying to process speech.
As such, most attempts to solve the problem of ambiguity and context in natural language
processing use knowledge based approaches, The difficulty with this though is that it
requires a huge amount of information to be processed.
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8. Anaphoric Referencing
An example of anaphoric referencing is something like this:
James arrived at the party but nobody saw him.
Him is anaphoric and refers back to James.
Anaphoric referencing is an essential way of constructing and maintaining conversations
without constant repetition and it poses problems for computational linguistics and natural
language processing because often it can be difficult to identify what the anaphoric
element actually refers to.
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9. Speed and Efficiency of Interface
For the best user experience, NLP interfaces need to be able to respond to queries as
quickly and efficiently as possible.
However because actually processing natural language correctly is a complex process,
actually producing an interface that can do it quickly and efficiently enough for users to
tolerate it, or better still have a good user experience, can be very difficult.
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10. Relevant Data
With any speech input into a NLP interface, there comes with it a lot of extra, yet
irrelevant information such as the gender and age of the speaker.
So the challenge for NLP is to distinguish between the relevant information needed to
correctly process the verbal input while simultaneously filtering out any irrelevant
information that isn't needed.
This is one area where processing natural language can be made quicker and more
efficient.
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11. Uses and Applications of NLP
Siri
Google Now
Siri is Apple’s almost infamous personal
assistant for the iOS operating system.
Siri uses NLP to answer questions and
make recommendations.
Google Now is essentially Google’s answer
to Siri, it is a personal assistant that uses
NLP to answer questions, make
recommendations and perform actions.
It was named the innovation of the year in
2012.
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12. Google’s NLP Research
Google is working on processing multiple natural languages at web scale, and they aim
to do this by leveraging the large amounts of data they have access to.
In true Google fashion this involves writing algorithms to predict things like: the part of
speech tags for each word in a sentence and the various relationships between them.
This handles the syntax of language.
To handle the semantics Google is working on solving problems like identifying noun
phrases in free text and what they refer to. They do this in free text, across documents
and against a knowledge base.
© Cloudspotting 2013
http://research.google.com/pubs/NaturalLanguageProcessing.html

13. Microsoft’s NLP Research
Microsoft is aiming to tackle NLP using a combination of knowledge engineered and
statistical machine learning techniques to remove the ambiguity in natural language.
The implications of this work are far reaching and could have an impact on applications
for “text critiquing, information retrieval (search), question answering, summarisation,
gaming and translation.”
In fact Microsoft’s NLP research and progress is already in use in many of their products
such as the grammar checkers in office, Encarta, Intellishrink and the Microsoft Product
Report.
© Cloudspotting 2013
http://research.microsoft.com/en-us/groups/nlp/

14. The Future of Search
Search is most definitely going to move away from structured, keyword based search
queries that search engines interpret using algorithms, towards more conversational and
unstructured search queries.
The implications are that hands free technology could really being to dominate the search
market by making voice search truly effective. Products like Apple’s iPhones and
Google’s Glass could begin to replace other technologies that do not offer conversational,
voice search.
This means that the primary way we interact with technology is developing and changing
and therefore so is the way we search and discover information.
© Cloudspotting 2013
http://www.digitaltrends.com/web/everything-you-need-to-know-about-latent-search/

15. The Future of Technology
NLP has real benefit for end users as it will eliminate the need to formulate appropriate
search queries in order to return the results you want, instead you will simple be in
conversation with technology.
One major barrier between man and machine will be broken.
© Cloudspotting 2013

16. Contact Us
Email: info@cloudspotting.co.uk
Tel: 01132 341 542
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Google+.so get in touch and let us know your thoughts on the future of search and the future of
wearable technology.
© Cloudspotting 2013