Natural Language Processing (NLP), Search and Wearable Technology


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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.

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Natural Language Processing (NLP), Search and Wearable Technology

  1. 1. Cloudspotting Presentation Natural Language Processing (NLP) © Cloudspotting 2013
  2. 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. © Cloudspotting 2013
  3. 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. © Cloudspotting 2013 (Image)
  4. 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. 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
  6. 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.) © Cloudspotting 2013
  7. 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. © Cloudspotting 2013
  8. 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. © Cloudspotting 2013
  9. 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. © Cloudspotting 2013
  10. 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. © Cloudspotting 2013
  11. 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. © Cloudspotting 2013
  12. 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
  13. 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
  14. 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
  15. 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. 16. Contact Us Email: Tel: 01132 341 542 You can also follow us on Twitter @LeedsWebAgency and find us on Facebook, Linked In and get in touch and let us know your thoughts on the future of search and the future of wearable technology. © Cloudspotting 2013