How to Write a Summary of an Article? Natural language processing A survey of related studies was conducted by the researchers in order to provide more insight into the research in the field of an experimentation and to get support of the Borer-Moore string searching algorithm as a relevant string matching algorithm that can be integrated with Natural Language Processing method and why it creates a better string searching process.
History[ edit ] The history of natural language processing generally started in the s, although work can be found from earlier periods. InAlan Turing published an article titled " Intelligence " which proposed what is now called the Turing test as a criterion of intelligence.
The Georgetown experiment in involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. Little further research in machine translation was conducted until the late s, when the first statistical machine translation Language processing essay were developed.
Some notably successful natural language processing systems developed in the s were SHRDLUa natural language system working in restricted " blocks worlds " with restricted vocabularies, and ELIZAa simulation of a Rogerian psychotherapistwritten by Joseph Weizenbaum between and Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction.
When the "patient" exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts? During the s, many programmers began to write "conceptual ontologies ", which structured real-world information into computer-understandable data.
Up to the s, Language processing essay natural language processing systems were based on complex sets of hand-written rules. Starting in the late s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing.
However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical modelswhich make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models.
Such models are generally more robust when given unfamiliar input, especially input that contains errors as is very common for real-world dataand produce more reliable results when integrated into a larger system comprising multiple subtasks.
Many of the notable early successes occurred in the field of machine translationdue especially to work at IBM Research, where successively more complicated statistical models were developed.
These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government.
However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was and often continues to be a major limitation in the success of these systems. As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data.
Recent research has increasingly focused on unsupervised and semi-supervised learning algorithms. Such algorithms are able to learn from data that has not been hand-annotated with the desired answers, or using a combination of annotated and non-annotated data.
Generally, this task is much more difficult than supervised learningand typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available including, among other things, the entire content of the World Wide Webwhich can often make up for the inferior results if the algorithm used has a low enough time complexity to be practical, which some such as Chinese Whispers do.
Statistical natural language processing SNLP [ edit ] Since the so-called "statistical revolution"   in the late s and mid s, much natural language processing research has relied heavily on machine learning.
Formerly, many language-processing tasks typically involved the direct hand coding of rules,   which is not in general robust to natural language variation. The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large corpora of typical real-world examples a corpus plural, "corpora" is a set of documents, possibly with human or computer annotations.
Many different classes of machine learning algorithms have been applied to natural language processing tasks.
These algorithms take as input a large set of "features" that are generated from the input data. Some of the earliest-used algorithms, such as decision treesproduced systems of hard if-then rules similar to the systems of hand-written rules that were then common.
Increasingly, however, research has focused on statistical modelswhich make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system.
Systems based on machine-learning algorithms have many advantages over hand-produced rules: The learning procedures used during machine learning automatically focus on the most common cases, whereas when writing rules by hand it is often not at all obvious where the effort should be directed.
Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to unfamiliar input e. Generally, handling such input gracefully with hand-written rules—or more generally, creating systems of hand-written rules that make soft decisions—is extremely difficult, error-prone and time-consuming.
Systems based on automatically learning the rules can be made more accurate simply by supplying more input data. However, systems based on hand-written rules can only be made more accurate by increasing the complexity of the rules, which is a much more difficult task.
In particular, there is a limit to the complexity of systems based on hand-crafted rules, beyond which the systems become more and more unmanageable. However, creating more data to input to machine-learning systems simply requires a corresponding increase in the number of man-hours worked, generally without significant increases in the complexity of the annotation process.
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