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Statistical parsing is a group of parsing methods within natural language processing. The methods have in common that they associate grammar rules with a probability. Grammar rules are traditionally viewed in computational linguistics as defining the valid sentences in a language. Within this mindset, the idea of associating each rule with a probability then provides the relative frequency of any given grammar rule and, by deduction, the probability of a complete parse for a sentence. (The probability associated with a grammar rule may be induced, but the application of that grammar rule within a parse tree and the computation of the probability of the parse tree based on its component rules is a form of deduction.) Using this concept, statistical parsers make use of a procedure to search over a space of all candidate parses, and the computation of each candidate's probability, to derive the most probable parse of a sentence. The Viterbi algorithm is one popular method of searching for the most probable parse.
"Search" in this context is an application of search algorithms in artificial intelligence.
As an example, think about the sentence "The can can hold water". A reader would instantly see that there is an object called "the can" and that this object is performing the action 'can' (i.e. is able to); and the thing the object is able to do is "hold"; and the thing the object is able to hold is "water". Using more linguistic terminology, "The can" is a noun phrase composed of a determiner followed by a noun, and "can hold water" is a verb phrase which is itself composed of a verb followed by a verb phrase. But is this the only interpretation of the sentence? Certainly "The can can" is a perfectly valid noun-phrase referring to a type of dance, and "hold water" is also a valid verb-phrase, although the coerced meaning of the combined sentence is non-obvious. This lack of meaning is not seen as a problem by most linguists (for a discussion on this point, see Colorless green ideas sleep furiously) but from a pragmatic point of view it is desirable to obtain the first interpretation rather than the second and statistical parsers achieve this by ranking the interpretations based on their probability.
(In this example various assumptions about the grammar have been made, such as a simple left-to-right derivation rather than head-driven, its use of noun-phrases rather than the currently fashionable determiner-phrases, and no type-check preventing a concrete noun being combined with an abstract verb phrase. None of these assumptions affect the thesis of the argument and a comparable argument can be made using any other grammatical formalism.)
There are a number of methods that statistical parsing algorithms frequently use. While few algorithms will use all of these they give a good overview of the general field. Most statistical parsing algorithms are based on a modified form of chart parsing. The modifications are necessary to support an extremely large number of grammatical rules and therefore search space, and essentially involve applying classical artificial intelligence algorithms to the traditionally exhaustive search. Some examples of the optimisations are only searching a likely subset of the search space (stack search), for optimising the search probability (Baum-Welch algorithm) and for discarding parses that are too similar to be treated separately (Viterbi algorithm).
In linguistics, syntax is the study of how words and morphemes combine to form larger units such as phrases and sentences. Central concerns of syntax include word order, grammatical relations, hierarchical sentence structure (constituency), agreement, the nature of crosslinguistic variation, and the relationship between form and meaning (semantics). There are numerous approaches to syntax that differ in their central assumptions and goals.
English grammar is the set of structural rules of the English language. This includes the structure of words, phrases, clauses, sentences, and whole texts.
A noun phrase – or NP or nominal (phrase) – is a phrase that usually has a noun or pronoun as its head, and has the same grammatical functions as a noun. Noun phrases are very common cross-linguistically, and they may be the most frequently occurring phrase type.
Linguistics is the scientific study of human language. Someone who engages in this study is called a linguist. See also the Outline of linguistics, the List of phonetics topics, the List of linguists, and the List of cognitive science topics. Articles related to linguistics include:
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.
Head-driven phrase structure grammar (HPSG) is a highly lexicalized, constraint-based grammar developed by Carl Pollard and Ivan Sag. It is a type of phrase structure grammar, as opposed to a dependency grammar, and it is the immediate successor to generalized phrase structure grammar. HPSG draws from other fields such as computer science and uses Ferdinand de Saussure's notion of the sign. It uses a uniform formalism and is organized in a modular way which makes it attractive for natural language processing.
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".
Generative grammar is a theoretical approach in linguistics that regards grammar as a domain-specific system of rules that generates all and only the grammatical sentences of a given language. In light of poverty of the stimulus arguments, grammar is regarded as being partly innate, the innate portion of the system being referred to as universal grammar. The generative approach has focused on the study of syntax while addressing other aspects of language including semantics, morphology, phonology, and psycholinguistics.
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 sentence diagram is a pictorial representation of the grammatical structure of a sentence. The term "sentence diagram" is used more when teaching written language, where sentences are diagrammed. The model shows the relations between words and the nature of sentence structure and can be used as a tool to help recognize which potential sentences are actual sentences.
Shallow parsing is an analysis of a sentence which first identifies constituent parts of sentences and then links them to higher order units that have discrete grammatical meanings. While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns, approaches that use machine learning techniques can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents. That is, these more advanced methods get around the problem that combinations of elementary constituents can have different higher level meanings depending on the context of the sentence.
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. 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.
Grammar induction is the process in machine learning of learning a formal grammar from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs.
Statistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach.
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.
Attempto Controlled English (ACE) is a controlled natural language, i.e. a subset of standard English with a restricted syntax and restricted semantics described by a small set of construction and interpretation rules. It has been under development at the University of Zurich since 1995. In 2013, ACE version 6.7 was announced.
Saliba is an Oceanic language spoken on the islets off the southeastern tip of Papua New Guinea. There are approximately 2,500 speakers of Saliba. Significant documentation of the language was undertaken by the Saliba-Logea documentation project, and hundreds of audio-video resources can be found in the project archive.
Syntactic parsing is the automatic analysis of syntactic structure of natural language, especially syntactic relations and labelling spans of constituents. It is motivated by the problem of structural ambiguity in natural language: a sentence can be assigned multiple grammatical parses, so some kind of knowledge beyond computational grammar rules is needed to tell which parse is intended. Syntactic parsing is one of the important tasks in computational linguistics and natural language processing, and has been a subject of research since the mid-20th century with the advent of computers.