Archive for April, 2009

Speech Recognition is a branch of Artificial Intelligence that enables spoken communication between human and computer, but there are some difficulties in the attempt of getting a more or less acceptable interpretation of the message, because the coopertation between information from different sources (such as the acoustic, phonetic, semantic or pragmatic) is ambiguous and some mistakes are unavoidable in the process.

Nearly all the Speech synthesizers use libraries of speech sound. The creation of this dicctionarie is important, because it is important to recognize the word user uses. To make the recognition easier, here is a recognition of vowels and recognition of consonants, and also a noise masking (some movile phones, for example, can work when we “talk” to them, and if we are on the street, there must be something that makes the sound clear). But even if the system has these advantages, mistakes may not be avoided. Most speech recognition algorithms rely only on the sound of the individual words, and not on their context, so they don’t understand speech, but recognize words. Here is an example of what could happen:

The child wore a spider ring on Halloween.

He was an American spy during the war.

The sound of “spider ring” and “spy during” is exactly the same. We hear the correct words depending on the context, and is something that we do unconsciously.

There are many ways of application of this system, but I think that the fact people with disabilities benefit from it is the most interesting. Some of them are unable to use their hands, others are deaf and use deaf telephony (voicemail to text, realy services or captioned telephone), and others have learning disabilities. There’s no doubt that our life will be easier in some years’ time when this systems get better.


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When we search for information on the net we can obtain it from different places and in different ways, for there is loads of available data on the internet.

Question answering, also known as QA, is a way of getting that information; this system should be able to answer our questions (done in natural language), searching in pre-structured database or documents written in natural language.

As Dell Zhang and Wee Sun Lee wrote in one article “it is important for an online question answering system to be practical, because it is time-consuming to download and analyze the original web documents”. A question answering system is another information retrieval system, but what QA systems do is supply just the information we need, not a list of possibilities as searching engines usually do. To obtain the answers, the QA systems combine some NLP techniques, because the answer depends on the type of question.

And as I have told, depending on the question, the methods used to find the answers are different. There are two methods: shallow and deep. The first one finds fragments of documents, filters the information based on the presence of the answer required, and then the answers are ordered based on different criteria, such as word order. If the way the question is formulated is not enough (or, for example, some of the questions based are classified with an incorrect type), the second method is used. “More sophisticated syntactic, semantic and contextual processing must be performed to extract or construct the answer”.

So, there have been many advances on this kind of information retrieval systems, but dealing with Natural Language with computers is quite difficult, and it can be hard to get the data we are looking for using that kind of language with systems that have to improve a lot.

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Here is the list of 10 research topics in major sites on Human Language Technologies I have chosen:

  1. Machine Translation
  2. Question answering systems
  3. Machine Learning in NLP
  4. Development of linguistic resources and tools
  5. Reconocimiento y síntesis de voz (Speech Recognition and Synthesis)
  6. Intelligent systems for natural language interaction
  7. Information retrieval, question answering, and information extraction
  8. Monolingual and multilingual text generation
  9. Lexical semantics and word sense disambiguation
  10. Human factors in MT and user interfaces

I’ll write one article for each topic that I have put in bold.

Topics taken from:

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