Natural Language Processing (NLP), Search and Wearable Technology

© Cloudspotting 2013

Cloudspotting Presentation Natural Language Processing

(NLP)



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.

What is Natural Language Processing?

http://searchcontentmanagement.techtarget.com/definition/natural-language-processing-NLP










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.

What is Natural Language Processing?


http://eil.stanford.edu/xpan/natural_language_processing_files/image002.gif (Image)










http://eil.stanford.edu/xpan/natural_language_processing_files/image002.gif


• 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.

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…

What are the Challenges of NLP?

http://language.worldofcomputing.net/nlp-overview/open-problems-in-natural-langauge-processing.html















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.)

Meeting Expectations

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http://wpuploads.appadvice.com/wp-content/uploads/2013/09/140.jpg





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.

Understanding Ambiguity & Context


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.

Anaphoric Referencing


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.

Speed and Efficiency of Interface


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.

Relevant Data


Siri is Apple’s almost infamous personal

assistant for the iOS operating system.

Siri uses NLP to answer questions and

make recommendations.

Uses and Applications of NLP

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.

Siri Google Now


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.

Google’s NLP Research

http://research.google.com/pubs/NaturalLanguageProcessing.html

http://research.google.com/pubs/NaturalLanguageProcessing.html


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.

Microsoft’s NLP Research

http://research.microsoft.com/en-us/groups/nlp/






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.

The Future of Search

http://www.digitaltrends.com/web/everything-you-need-to-know-about-latent-search/

















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.

The Future of Technology


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Natural Language Processing (NLP), Search and Wearable Technology

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Transcript of Natural Language Processing (NLP), Search and Wearable Technology