Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
A noun is a word that functions as the name of a specific object or set of objects, such as living creatures, places, actions, qualities, states of existence, or ideas. However, noun is not a semantic category, so it cannot be characterized in terms of its meaning. Thus, actions and states of existence can also be expressed by verbs, qualities by adjectives, and places by adverbs. Linguistically, a noun is a member of a large, open part of speech whose members can occur as the main word in the subject of a clause, the object of a verb, or the object of a preposition.
In syntax and grammar, a phrase is a group of words which act together as a grammatical unit. For instance, the English expression "the very happy squirrel" is a noun phrase which contains the adjective phrase "very happy". Phrases can consist of a single word or a complete sentence. In theoretical linguistics, phrases are often analyzed as units of syntactic structure such as a constituent.
In traditional grammar, a part of speech or part-of-speech is a category of words that have similar grammatical properties. Words that are assigned to the same part of speech generally display similar syntactic behavior—they play similar roles within the grammatical structure of sentences—and sometimes similar morphology in that they undergo inflection for similar properties.
Lexical functional grammar (LFG) is a constraint-based grammar framework in theoretical linguistics. It posits two separate levels of syntactic structure, a phrase structure grammar representation of word order and constituency, and a representation of grammatical functions such as subject and object, similar to dependency grammar. The development of the theory was initiated by Joan Bresnan and Ronald Kaplan in the 1970s, in reaction to the theory of transformational grammar which was current in the late 1970s. It mainly focuses on syntax, including its relation with morphology and semantics. There has been little LFG work on phonology.
Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The term parsing comes from Latin pars (orationis), meaning part.
A garden-path sentence is a grammatically correct sentence that starts in such a way that a reader's most likely interpretation will be incorrect; the reader is lured into a parse that turns out to be a dead end or yields a clearly unintended meaning. "Garden path" refers to the saying "to be led down [or up] the garden path", meaning to be deceived, tricked, or seduced. In A Dictionary of Modern English Usage (1926), Fowler describes such sentences as unwittingly laying a "false scent".
Link grammar (LG) is a theory of syntax by Davy Temperley and Daniel Sleator which builds relations between pairs of words, rather than constructing constituents in a phrase structure hierarchy. Link grammar is similar to dependency grammar, but dependency grammar includes a head-dependent relationship, whereas Link Grammar makes the head-dependent relationship optional. Colored Multiplanar Link Grammar (CMLG) is an extension of LG allowing crossing relations between pairs of words. The relationship between words is indicated with link types, thus making the Link grammar closely related to certain categorial grammars.
In corpus linguistics, part-of-speech tagging, also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.
A grammatical category or grammatical feature is a property of items within the grammar of a language. Within each category there are two or more possible values, which are normally mutually exclusive. Frequently encountered grammatical categories include:
A definite clause grammar (DCG) is a way of expressing grammar, either for natural or formal languages, in a logic programming language such as Prolog. It is closely related to the concept of attribute grammars / affix grammars from which Prolog was originally developed. DCGs are usually associated with Prolog, but similar languages such as Mercury also include DCGs. They are called definite clause grammars because they represent a grammar as a set of definite clauses in first-order logic.
In linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data. The exploitation of treebank data has been important ever since the first large-scale treebank, The Penn Treebank, was published. However, although originating in computational linguistics, the value of treebanks is becoming more widely appreciated in linguistics research as a whole. For example, annotated treebank data has been crucial in syntactic research to test linguistic theories of sentence structure against large quantities of naturally occurring examples.
Kalaba-X is a simple constructed language created by the American linguist Kenneth L. Pike to help with the teaching of translation techniques.
Constraint grammar (CG) is a methodological paradigm for natural language processing (NLP). Linguist-written, context dependent rules are compiled into a grammar that assigns grammatical tags ("readings") to words or other tokens in running text. Typical tags address lemmatisation, inflexion, derivation, syntactic function, dependency, valency, case roles, semantic type etc. Each rule either adds, removes, selects or replaces a tag or a set of grammatical tags in a given sentence context. Context conditions can be linked to any tag or tag set of any word anywhere in the sentence, either locally or globally. Context conditions in the same rule may be linked, i.e. conditioned upon each other, negated, or blocked by interfering words or tags. Typical CGs consist of thousands of rules, that are applied set-wise in progressive steps, covering ever more advanced levels of analysis. Within each level, safe rules are used before heuristic rules, and no rule is allowed to remove the last reading of a given kind, thus providing a high degree of robustness.
Bootstrapping is a term used in language acquisition in the field of linguistics. It refers to the idea that humans are born innately equipped with a mental faculty that forms the basis of language. It is this language faculty that allows children to effortlessly acquire language. As a process, bootstrapping can be divided into different domains, according to whether it involves semantic bootstrapping, syntactic bootstrapping, prosodic bootstrapping, or pragmatic bootstrapping.
Linguistic categories include
Transfer-based machine translation is a type of machine translation (MT). It is currently one of the most widely used methods of machine translation. In contrast to the simpler direct model of MT, transfer MT breaks translation into three steps: analysis of the source language text to determine its grammatical structure, transfer of the resulting structure to a structure suitable for generating text in the target language, and finally generation of this text. Transfer-based MT systems are thus capable of using knowledge of the source and target languages.
Rule-based machine translation is machine translation systems based on linguistic information about source and target languages basically retrieved from dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively. Having input sentences, an RBMT system generates them to output sentences on the basis of morphological, syntactic, and semantic analysis of both the source and the target languages involved in a concrete translation task.
Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory. Deep linguistic processing approaches differ from "shallower" methods in that they yield more expressive and structural representations which directly capture long-distance dependencies and underlying predicate-argument structures.
The knowledge-intensive approach of deep linguistic processing requires considerable computational power, and has in the past sometimes been judged as being intractable. However, research in the early 2000s had made considerable advancement in efficiency of deep processing. Today, efficiency is no longer a major problem for applications using deep linguistic processing.
The following outline is provided as an overview of and topical guide to natural language processing:
