| Natural Language Processing |
Article Index for Natural Language |
Website Links For Natural Language Processing |
Information AboutNatural Language Processing |
| CATEGORIES ABOUT NATURAL LANGUAGE PROCESSING | |
| computational linguistics | |
| speech recognition | |
| natural language processingcomputational linguistics | |
| speech recognition | |
| natural language processing | |
| artificial intelligence | |
| computational linguistics | |
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TASKS AND LIMITATIONS In theory, natural language processing is a very attractive method of Human-computer Interaction . Early systems such as SHRDLU , working in restricted " Blocks World s" with restricted vocabularies, worked extremely well, leading researchers to excessive optimism which was soon lost when the systems were extended to more realistic situations with real-world ambiguity and complexity. Natural language understanding is sometimes referred to as an AI-complete problem, because natural language recognition seems to require extensive knowledge about the outside world and the ability to manipulate it. The definition of "understanding" is one of the major problems in natural language processing. CONCRETE PROBLEMS Some examples of the problems faced by natural language understanding systems:
English is particularly challenging in this regard because it has little Inflectional Morphology to distinguish between parts of speech.
SUBPROBLEMS ; Speech Segmentation : In most spoken languages, the sounds representing successive letters blend into each other, so the conversion of the analog signal to discrete characters can be a very difficult process. Also, in natural speech there are hardly any pauses between successive words; the location of those boundaries usually must take into account grammatical and semantical constraints, as well as the context. ; , Japanese and Thai do not have single word boundaries either, so any significant text parsing usually requires the identification of word boundaries, which is often a non-trivial task. ; Word Sense Disambiguation : Many words have more than one meaning; we have to select the meaning which makes the most sense in context. ; for Natural Language s is Ambiguous , i.e. there are often multiple possible Parse Tree s for a given sentence. Choosing the most appropriate one usually requires Semantic and contextual information. Specific problem components of syntactic ambiguity include Sentence Boundary Disambiguation . ; Imperfect or irregular input : Foreign or regional accents and vocal impediments in speech; typing or grammatical errors, OCR errors in texts. ; Speech Acts and plans: Sentences often don't mean what they literally say; for instance a good answer to "Can you pass the salt" is to pass the salt; in most contexts "Yes" is not a good answer, although "No" is better and "I'm afraid that I can't see it" is better yet. Or again, if a class was not offered last year, "The class was not offered last year" is a better answer to the question "How many students failed the class last year?" than "None" is. STATISTICAL NLP Statistical natural language processing uses Stochastic , Probabilistic and Statistical methods to resolve some of the difficulties discussed above, especially those which arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses. Methods for disambiguation often involve the use of Corpora and Markov Model s. The technology for statistical NLP comes mainly from Machine Learning and Data Mining , both of which are fields of Artificial Intelligence that involve learning from data. MAJOR TASKS IN NLP
EVALUATION OF NATURAL LANGUAGE PROCESSING The goal of NLP evaluation is to measure one or more ''qualities'' of an algorithm or a system, in order to determine if (or to what extent) the system answers the goals of its designers, or the needs of its users. Research in NLP evaluation has received considerable attention, because the definition of proper evaluation criteria is one way to specify precisely an NLP problem, going thus beyond the vagueness of tasks defined only as ''language understanding'' or ''language generation''. A precise set of evaluation criteria, which includes mainly evaluation data and evaluation metrics, enables several teams to compare their solutions to a given NLP problem.
... Depending on the evaluation procedures, a number of distinctions are traditionally made in NLP evaluation.
Intrinsic evaluation considers an isolated NLP system and characterizes its performance mainly with respect to a ''gold standard'' result, pre-defined by the evaluators. Extrinsic evaluation, also called ''evaluation in use'' considers the NLP system in a more complex setting, either as an embedded system or serving a precise function for a human user. The extrinsic performance of the system is then characterized in terms of its utility with respect to the overall task of the complex system or the human user.
Black-box evaluation requires one to run an NLP system on a given data set and to measure a number of parameters related to the quality of the process (speed, reliability, resource consumption) and, most importantly, to the quality of the result (e.g. the accuracy of data annotation or the fidelity of a translation). Glass-box evaluation looks at the design of the system, the algorithms that are implemented, the linguistic resources it uses (e.g. vocabulary size), etc. Given the complexity of NLP problems, it is often difficult to predict performance only on the basis of glass-box evaluation, but this type of evaluation is more informative with respect to error analysis or future developments of a system.
In many cases, automatic procedures can be defined to evaluate an NLP system by comparing its output with the gold standard (or desired) one. Although the cost of producing the gold standard can be quite high, automatic evaluation can be repeated as often as needed without much additional costs (on the same input data). However, for many NLP problems, the definition of a gold standard is a complex task, and can prove impossible when inter-annotator agreement is insufficient. Manual evaluation is performed by human judges, which are instructed to estimate the quality of a system, or most often of a sample of its output, based on a number of criteria. Although, thanks to their linguistic competence, human judges can be considered as the reference for a number of language processing tasks, there is also considerable variation across their ratings. This is why automatic evaluation is sometimes referred to as ''objective'' evaluation, while the human kind appears to be more ''subjective.''
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