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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. [1]
The term treebank was coined by linguist Geoffrey Leech in the 1980s, by analogy to other repositories such as a seedbank or bloodbank. [2] This is because both syntactic and semantic structure are commonly represented compositionally as a tree structure. The term parsed corpus is often used interchangeably with the term treebank, with the emphasis on the primacy of sentences rather than trees.
Treebanks are often created on top of a corpus that has already been annotated with part-of-speech tags. In turn, treebanks are sometimes enhanced with semantic or other linguistic information. Treebanks can be created completely manually, where linguists annotate each sentence with syntactic structure, or semi-automatically, where a parser assigns some syntactic structure which linguists then check and, if necessary, correct. In practice, fully checking and completing the parsing of natural language corpora is a labour-intensive project that can take teams of graduate linguists several years. The level of annotation detail and the breadth of the linguistic sample determine the difficulty of the task and the length of time required to build a treebank.
Some treebanks follow a specific linguistic theory in their syntactic annotation (e.g. the BulTreeBank follows HPSG) but most try to be less theory-specific. However, two main groups can be distinguished: treebanks that annotate phrase structure (for example the Penn Treebank or ICE-GB) and those that annotate dependency structure (for example the Prague Dependency Treebank or the Quranic Arabic Dependency Treebank).
It is important to clarify the distinction between the formal representation and the file format used to store the annotated data. Treebanks are necessarily constructed according to a particular grammar. The same grammar may be implemented by different file formats. For example, the syntactic analysis for John loves Mary, shown in the figure on the right, may be represented by simple labelled brackets in a text file, like this (following the Penn Treebank notation):
(S (NP (NNP John)) (VP (VPZ loves) (NP (NNP Mary))) (. .))
This type of representation is popular because it is light on resources, and the tree structure is relatively easy to read without software tools. However, as corpora become increasingly complex, other file formats may be preferred. Alternatives include treebank-specific XML schemes, numbered indentation and various types of standoff notation.
From a computational linguistics [3] perspective, treebanks have been used to engineer state-of-the-art natural language processing systems such as part-of-speech taggers, parsers, semantic analyzers and machine translation systems. [4] Most computational systems utilize gold-standard treebank data. However, an automatically parsed corpus that is not corrected by human linguists can still be useful. It can provide evidence of rule frequency for a parser. A parser may be improved by applying it to large amounts of text and gathering rule frequencies. However, it should be obvious that only by a process of correcting and completing a corpus by hand is it possible then to identify rules absent from the parser knowledge base. In addition, frequencies are likely to be more accurate.
In corpus linguistics, treebanks are used to study syntactic phenomena (for example, diachronic corpora can be used to study the time course of syntactic change). Once parsed, a corpus will contain frequency evidence showing how common different grammatical structures are in use. Treebanks also provide evidence of coverage and support the discovery of new, unanticipated, grammatical phenomena.
Another use of treebanks in theoretical linguistics and psycholinguistics is interaction evidence. A completed treebank can help linguists carry out experiments as to how the decision to use one grammatical construction tends to influence the decision to form others, and to try to understand how speakers and writers make decisions as they form sentences. Interaction research is particularly fruitful as further layers of annotation, e.g. semantic, pragmatic, are added to a corpus. It is then possible to evaluate the impact of non-syntactic phenomena on grammatical choices.
In linguistics research, annotated treebank data has been used in syntactic research to test linguistic theories of sentence structure against large quantities of naturally occurring examples.[ citation needed ]
A semantic treebank is a collection of natural language sentences annotated with a meaning representation. These resources use a formal representation of each sentence's semantic structure. Semantic treebanks vary in the depth of their semantic representation. A notable example of deep semantic annotation is the Groningen Meaning Bank, developed at the University of Groningen and annotated using Discourse Representation Theory. An example of a shallow semantic treebank is PropBank, which provides annotation of verbal propositions and their arguments, without attempting to represent every word in the corpus in logical form.
Many syntactic treebanks have been developed for a wide variety of languages:
To facilitate the further researches between multilingual tasks, some researchers discussed the universal annotation scheme for cross-languages. In this way, people try to utilize or merge the advantages of different treebanks corpora. For instance, The universal annotation approach for dependency treebanks; [10] and the universal annotation approach for phrase structure treebanks. [11]
One of the key ways to extract evidence from a treebank is through search tools. Search tools for parsed corpora typically depend on the annotation scheme that was applied to the corpus. User interfaces range in sophistication from expression-based query systems aimed at computer programmers to full exploration environments aimed at general linguists. Wallis (2008) discusses the principles of searching treebanks in detail and reviews the state of the art around that time. [12]
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches. 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.
Corpus linguistics is the study of a language as that language is expressed in its text corpus, its body of "real world" text. Corpus linguistics proposes that a reliable analysis of a language is more feasible with corpora collected in the field—the natural context ("realia") of that language—with minimal experimental interference. The large collections of text allow linguistics to run quantitative analyses on linguistic concepts, otherwise harder to quantify.
In linguistics and natural language processing, a corpus or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated.
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.
Generalized phrase structure grammar (GPSG) is a framework for describing the syntax and semantics of natural languages. It is a type of constraint-based phrase structure grammar. Constraint based grammars are based around defining certain syntactic processes as ungrammatical for a given language and assuming everything not thus dismissed is grammatical within that language. Phrase structure grammars base their framework on constituency relationships, seeing the words in a sentence as ranked, with some words dominating the others. For example, in the sentence "The dog runs", "runs" is seen as dominating "dog" since it is the main focus of the sentence. This view stands in contrast to dependency grammars, which base their assumed structure on the relationship between a single word in a sentence and its dependents.
Charles J. Fillmore was an American linguist and Professor of Linguistics at the University of California, Berkeley. He received his Ph.D. in Linguistics from the University of Michigan in 1961. Fillmore spent ten years at Ohio State University and a year as a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University before joining Berkeley's Department of Linguistics in 1971. Fillmore was extremely influential in the areas of syntax and lexical semantics.
The American National Corpus (ANC) is a text corpus of American English containing 22 million words of written and spoken data produced since 1990. Currently, the ANC includes a range of genres, including emerging genres such as email, tweets, and web data that are not included in earlier corpora such as the British National Corpus. It is annotated for part of speech and lemma, shallow parse, and named entities.
Linguistic categories include
The International Corpus of English (ICE) is a set of text corpora representing varieties of English from around the world. Over twenty countries or groups of countries where English is the first language or an official second language are included.
Eckhard Bick is a German-born Esperantist who studied medicine in Bonn but now works as a researcher in computational linguistics. He was active in an Esperanto youth group in Bonn and in the Germana Esperanto-Junularo, a nationwide Esperanto youth federation. Since his marriage to a Danish woman he and his family live in Denmark.
The Quranic Arabic Corpus is an annotated linguistic resource consisting of 77,430 words of Quranic Arabic. The project aims to provide morphological and syntactic annotations for researchers wanting to study the language of the Quran.
Deep Linguistic Processing with HPSG - INitiative (DELPH-IN) is a collaboration where computational linguists worldwide develop natural language processing tools for deep linguistic processing of human language. The goal of DELPH-IN is to combine linguistic and statistical processing methods in order to computationally understand the meaning of texts and utterances.
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:
Manning's Law describes the combination of principles that need to be balanced in the design and growth of universal linguistic dependencies. These dependencies are used to describe and model syntactic relations, for all languages. This supports natural language processing, and is a major topic, with its own event, thousands of linguistics and AI researchers working with and on it, and widely-adopted. The law was put forward by Christopher D. Manning.
Universal Dependencies, frequently abbreviated as UD, is an international cooperative project to create treebanks of the world's languages. These treebanks are openly accessible and available. Core applications are automated text processing in the field of natural language processing (NLP) and research into natural language syntax and grammar, especially within linguistic typology. The project's primary aim is to achieve cross-linguistic consistency of annotation, while still permitting language-specific extensions when necessary. The annotation scheme has it roots in three related projects: Stanford Dependencies, Google universal part-of-speech tags, and the Interset interlingua for morphosyntactic tagsets. The UD annotation scheme uses a representation in the form of dependency trees as opposed to a phrase structure trees. At the present time, there are just over 200 treebanks of more than 100 languages available in the UD inventory.
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.
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