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Origins[ edit ] Computational linguistics is often grouped within the field of artificial intelligence, but actually was present before the development of artificial intelligence. Computational linguistics originated with efforts in the United States in the s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English.
Earlier methods such as lexicostatistics and glottochronology have been proven to be premature and inaccurate. However, recent interdisciplinary studies which borrow concepts from biological studies, especially gene mappinghave proved to produce more sophisticated analytical tools and more trustworthy results.
Computational linguistics was born as the name of the new field of study devoted to developing algorithms and software for intelligently processing language data. The term "computational linguistics" itself was first coined by David Haysfounding member of both the Association for Computational Linguistics and the International Committee on Computational Linguistics.
Thus, what started as an effort to translate between languages evolved into an entire discipline devoted to understanding how to represent and process natural languages using computers.
Programs meant for human-machine communication are called conversational agents. The following sections discuss some of the literature available across the entire field broken into four main area of discourse: Developmental approaches[ edit ] Language is a cognitive skill which develops throughout the life of an individual.
This developmental process has been examined using a number of techniques, and a computational approach is one of them. Human language development does provide some constraints which make it harder to apply a computational method to understanding it.
For instance, during language acquisitionhuman children are largely only exposed to positive evidence. This is insufficient information for a simple hypothesis testing procedure for information as complex as language,  and so provides certain boundaries for a computational approach to modeling language development and acquisition in an individual.
Attempts have been made to model the developmental process of language acquisition in children from a computational angle, leading to both statistical grammars and connectionist models.
Using models, it has been shown that languages can be learned with a combination of simple input presented incrementally as the child develops better memory and longer attention span. The ability of infants to develop language has also been modeled using robots  in order to test linguistic theories.
Enabled to learn as children might, a model was created based on an affordance model in which mappings between actions, perceptions, and effects were created and linked to spoken words. Crucially, these robots were able to acquire functioning word-to-meaning mappings without needing grammatical structure, vastly simplifying the learning process and shedding light on information which furthers the current understanding of linguistic development.
It is important to note that this information could only have been empirically tested using a computational approach. As our understanding of the linguistic development of an individual within a lifetime is continually improved using neural networks and learning robotic systemsit is also important to keep in mind that languages themselves change and develop through time.
Computational approaches to understanding this phenomenon have unearthed very interesting information. It is clear that the understanding of linguistic development in humans as well as throughout evolutionary time has been fantastically improved because of advances in computational linguistics.
The ability to model and modify systems at will affords science an ethical method of testing hypotheses that would otherwise be intractable.
To this end, the English language has been meticulously studied using computational approaches to better understand how the language works on a structural level.
One of the most important pieces of being able to study linguistic structure is the availability of large linguistic corpora, or samples. This grants computational linguists the raw data necessary to run their models and gain a better understanding of the underlying structures present in the vast amount of data which is contained in any single language.
One of the most cited English linguistic corpora is the Penn Treebank.The CPL is an interdepartmental neuroscience research laboratory at Cornell University in Ithaca, NY.. Work in our lab centers around questions about neural coding, neuromodulation, and the neural bases of learning and memory processes, and is mostly based in the olfactory systems of rats and mice.
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