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Natural language processing

natural language processing, natural language processing pdf
Natural language processing is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human natural languages As such, NLP is related to the area of human–computer interaction Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation

An automated online assistant providing customer service on a web page, an example of an application where natural language processing is a major component


  • 1 History
  • 2 Using machine learning
  • 3 Major tasks
  • 4 Statistical
  • 5 Evaluation
    • 51 Objectives
    • 52 Timeline of evaluation
    • 53 Different types of evaluation
  • 6 Standardization
  • 7 See also
  • 8 References
  • 9 Further reading


Main article: History of natural language processing

The history of NLP generally starts in the 1950s, although work can be found from earlier periods In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence

The Georgetown experiment in 1954 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 However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed

Some notably successful NLP systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966 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 1970s many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data Examples are MARGIE Schank, 1975, SAM Cullingford, 1978, PAM Wilensky, 1978, TaleSpin Meehan, 1976, QUALM Lehnert, 1977, Politics Carbonell, 1979, and Plot Units Lehnert 1981 During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky

Up to the 1980s, most NLP systems were based on complex sets of hand-written rules Starting in the late 1980s, however, there was a revolution in NLP with the introduction of machine learning algorithms for language processing This was due to both the steady increase in computational power see Moore's Law and the gradual lessening of the dominance of Chomskyan theories of linguistics eg transformational grammar, whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules However, part-of-speech tagging introduced the use of hidden Markov models to NLP, and increasingly, research has focused on statistical models, which 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 data, and 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 translation, due 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 learning, and 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 Web, which can often make up for the inferior results

Using machine learning

Modern NLP algorithms are based on machine learning, especially statistical machine learning The paradigm of machine learning is different from that of most prior attempts at language processing Prior implementations of language-processing tasks typically involved the direct hand coding of large sets of rules The machine-learning paradigm calls instead for using general learning algorithms — often, although not always, grounded in 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 or sometimes, individual sentences that have been hand-annotated with the correct values to be learned

Many different classes of machine learning algorithms have been applied to NLP 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 trees, produced 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 models, which 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 eg containing words or structures that have not been seen before and to erroneous input eg with misspelled words or words accidentally omitted 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

The subfield of NLP devoted to learning approaches is known as natural language learning NLL and its conference CoNLL and peak body SIGNLL are sponsored by ACL, recognizing also their links with computational linguistics and Language Acquisition When the aim of computational language learning research is to understand more about human language acquisition, or psycholinguistics, NLL overlaps into the related field of computational psycholinguistics

Major tasks

The following is a list of some of the most commonly researched tasks in NLP Note that some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks What distinguishes these tasks from other potential and actual NLP tasks is not only the volume of research devoted to them but the fact that for each one there is typically a well-defined problem setting, a standard metric for evaluating the task, standard corpora on which the task can be evaluated, and competitions devoted to the specific task

Automatic summarization Produce a readable summary of a chunk of text Often used to provide summaries of text of a known type, such as articles in the financial section of a newspaper Coreference resolution Given a sentence or larger chunk of text, determine which words "mentions" refer to the same objects "entities" Anaphora resolution is a specific example of this task, and is specifically concerned with matching up pronouns with the nouns or names that they refer to The more general task of coreference resolution also includes identifying so-called "bridging relationships" involving referring expressions For example, in a sentence such as "He entered John's house through the front door", "the front door" is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John's house rather than of some other structure that might also be referred to Discourse analysis This rubric includes a number of related tasks One task is identifying the discourse structure of connected text, ie the nature of the discourse relationships between sentences eg elaboration, explanation, contrast Another possible task is recognizing and classifying the speech acts in a chunk of text eg yes-no question, content question, statement, assertion, etc Machine translation Automatically translate text from one human language to another This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", ie requiring all of the different types of knowledge that humans possess grammar, semantics, facts about the real world, etc in order to solve properly Morphological segmentation Separate words into individual morphemes and identify the class of the morphemes The difficulty of this task depends greatly on the complexity of the morphology ie the structure of words of the language being considered English has fairly simple morphology, especially inflectional morphology, and thus it is often possible to ignore this task entirely and simply model all possible forms of a word eg "open, opens, opened, opening" as separate words In languages such as Turkish or Manipuri, a highly agglutinated Indian language, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms Named entity recognition NER Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is eg person, location, organization Note that, although capitalization can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case is often inaccurate or insufficient For example, the first word of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized Furthermore, many other languages in non-Western scripts eg Chinese or Arabic do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names For example, German capitalizes all nouns, regardless of whether they refer to names, and French and Spanish do not capitalize names that serve as adjectives Natural language generation Convert information from computer databases or semantic intents into readable human language Natural language understanding Convert chunks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural languages concepts Introduction and creation of language metamodel and ontology are efficient however empirical solutions An explicit formalization of natural languages semantics without confusions with implicit assumptions such as closed-world assumption CWA vs open-world assumption, or subjective Yes/No vs objective True/False is expected for the construction of a basis of semantics formalization Optical character recognition OCR Given an image representing printed text, determine the corresponding text Part-of-speech tagging Given a sentence, determine the part of speech for each word Many words, especially common ones, can serve as multiple parts of speech For example, "book" can be a noun "the book on the table" or verb "to book a flight"; "set" can be a noun, verb or adjective; and "out" can be any of at least five different parts of speech Some languages have more such ambiguity than others Languages with little inflectional morphology, such as English are particularly prone to such ambiguity Chinese is prone to such ambiguity because it is a tonal language during verbalization Such inflection is not readily conveyed via the entities employed within the orthography to convey intended meaning Parsing Determine the parse tree grammatical analysis of a given sentence The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses In fact, perhaps surprisingly, for a typical sentence there may be thousands of potential parses most of which will seem completely nonsensical to a human Question answering Given a human-language question, determine its answer Typical questions have a specific right answer such as "What is the capital of Canada", but sometimes open-ended questions are also considered such as "What is the meaning of life" Recent works have looked at even more complex questions Relationship extraction Given a chunk of text, identify the relationships among named entities eg who is married to whom Sentence breaking also known as sentence boundary disambiguation Given a chunk of text, find the sentence boundaries Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes eg marking abbreviations Sentiment analysis Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects It is especially useful for identifying trends of public opinion in the social media, for the purpose of marketing Speech recognition Given a sound clip of a person or people speaking, determine the textual representation of the speech This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete" see above In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition see below Note also that in most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process Speech segmentation Given a sound clip of a person or people speaking, separate it into words A subtask of speech recognition and typically grouped with it Topic segmentation and recognition Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment Word segmentation Separate a chunk of continuous text into separate words For a language like English, this is fairly trivial, since words are usually separated by spaces However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language Word sense disambiguation Many words have more than one meaning; we have to select the meaning which makes the most sense in context For this problem, we are typically given a list of words and associated word senses, eg from a dictionary or from an online resource such as WordNet

In some cases, sets of related tasks are grouped into subfields of NLP that are often considered separately from NLP as a whole Examples include:

Information retrieval IR This is concerned with storing, searching and retrieving information It is a separate field within computer science closer to databases, but IR relies on some NLP methods for example, stemming Some current research and applications seek to bridge the gap between IR and NLP Information extraction IE This is concerned in general with the extraction of semantic information from text This covers tasks such as named entity recognition, Coreference resolution, relationship extraction, etc Speech processing This covers speech recognition, text-to-speech and related tasks

Other tasks include:

  • Native-language identification
  • Stemming
  • Text simplification
  • Text-to-speech
  • Text-proofing
  • Natural language search
  • Query expansion
  • Automated essay scoring
  • Truecasing


Main article: Stochastic grammar

Statistical natural-language processing uses stochastic, probabilistic, and statistical methods to resolve some of the difficulties discussed above, especially those which arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses Methods for disambiguation often involve the use of corpora and Markov models The ESPRIT Project P26 1984 - 1988, led by CSELT, explored the problem of speech recognition comparing knowledge-based approach and statistical ones: the chosen result was a completely statistical model One among the first models of statistical natural language understanding was introduced in 1991 by Roberto Pieraccini, Esther Levin, and Chin-Hui Lee from Bell Laboratories NLP comprises all quantitative approaches to automated language processing, including probabilistic modeling, information theory, and linear algebra The technology for statistical NLP comes mainly from machine learning and data mining, both of which are fields of artificial intelligence that involve learning from data



The goal of NLP evaluation is to measure one or more qualities of an algorithm or a system, in order to determine: whether the algorithm answers the goals of its designers, or if the system meets the needs of its users Research in NLP evaluation has received considerable attention, because the definition of proper evaluation criteria is one way to specify precisely an NLP problem The metric of NLP evaluation on an algorithmic system allows for the integration of language understanding and language generation A precise set of evaluation criteria, which include mainly evaluation data and evaluation metrics can enable several teams to compare their solutions for a given NLP problem

Timeline of evaluation

  • In 1983, start of Esprit P26 Project, that evaluated Speech Technologies including general topic such as Syntactic & Semantic Parsing, etc comparing rule-based towards statistical approaches
  • In 1987, the first evaluation campaign on written texts seems to be a campaign dedicated to message understanding Pallet 1998
  • The Parseval/GEIG project compared phrase-structure grammars Black 1991
  • There were series of campaigns within Tipster project on tasks like summarization, translation, and searching Hirschman 1998
  • In 1994, in Germany, the Morpholympics compared German morphological taggers
  • The Senseval & Romanseval campaigns were conducted with the objectives of semantic disambiguation
  • In 1996, the Sparkle campaign compared syntactic parsers in four different languages English, French, German and Italian
  • In France, the Grace project compared a set of 21 taggers for French in 1997 Adda 1999
  • In 2004, during the Technolangue/Easy project, 13 parsers for French were compared
  • Large-scale evaluation of dependency parsers were performed in the context of the CoNLL shared tasks in 2006 and 2007
  • In France, within the ANR-Passage project end of 2007, 10 parsers for French were compared – passage web site
  • In Italy, the EVALITA campaign was conducted in 2007, 2009, 2011, and 2014 to compare various NLP and speech tools for Italian – EVALITA web site

Different types of evaluation

Depending on the evaluation procedures, a number of distinctions are traditionally made in NLP evaluation

Intrinsic v extrinsic evaluation Intrinsic evaluation considers an isolated NLP system and characterizes its performance with respect to a gold standard result as defined by the evaluators Extrinsic evaluation, also called evaluation in use, considers the NLP system in a more complex setting as either an embedded system or a precise function for a human user The extrinsic performance of the system is then characterized in terms of utility with respect to the overall task of the extraneous system or the human user For example, consider a syntactic parser which is based on the output of some part of speech POS tagger An intrinsic evaluation would run the POS tagger on structured data, and compare the system output of the POS tagger to the gold standard output An extrinsic evaluation would run the parser with some other POS tagger, and then with the novel POS tagger, and compare the parsing accuracy Black-box v glass-box evaluation Black-box evaluation requires someone to run an NLP system on a sample data set and to measure a number of parameters related to: the quality of the process, such as speed, reliability, resource consumption; and most importantly, the quality of the result, such as the accuracy of data annotation or the fidelity of a translation Glass-box evaluation looks at the: design of the system; the algorithms that are implemented; the linguistic resources it uses, like vocabulary size or expression set cardinality Given the complexity of NLP problems, it is often difficult to predict performance only on the basis of glass-box evaluation; but this type of evaluation is more informative with respect to error analysis or future developments of a system Automatic v manual evaluation In many cases, automatic procedures can be defined to evaluate an NLP system by comparing its output with the gold standard one Although the cost of reproducing the gold standard can be quite high, bootstrapping automatic evaluation on the same input data can be repeated as often as needed without inordinate additional costs However, for many NLP problems the precise definition of a gold standard is a complex task and it can prove impossible when inter-annotator agreement is insufficient Manual evaluation is best performed by human judges instructed to estimate the quality of a system, or most often of a sample of its output, based on a number of criteria Although, thanks to their linguistic competence, human judges can be considered as the reference for a number of language processing tasks, there is also considerable variation across their ratings That is why automatic evaluation is sometimes referred to as objective evaluation while the human evaluation is perspective


An ISO subcommittee is working in order to ease interoperability between lexical resources and NLP programs The subcommittee is part of ISO/TC37 and is called ISO/TC37/SC4 Some ISO standards are already published but most of them are under construction, mainly on lexicon representation see LMF, annotation, and data category registry

See also

  • Biomedical text mining
  • Compound term processing
  • Computer-assisted reviewing
  • Controlled natural language
  • Deep linguistic processing
  • Foreign language reading aid
  • Foreign language writing aid
  • Language technology
  • Latent Dirichlet allocation LDA
  • Latent semantic indexing
  • List of natural language processing toolkits
  • LRE Map
  • Natural language programming
  • Reification linguistics
  • Semantic folding
  • Spoken dialogue system
  • Thought vector
  • Transderivational search
  • Word2vec


  1. ^ Implementing an online help desk system based on conversational agent Authors: Alisa Kongthon, Chatchawal Sangkeettrakarn, Sarawoot Kongyoung and Choochart Haruechaiyasak Published by ACM 2009 Article, Bibliometrics Data Bibliometrics Published in: Proceeding, MEDES '09 Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM New York, NY, USA ISBN 978-1-60558-829-2, doi:101145/16438231643908
  2. ^ Hutchins, J 2005 "The history of machine translation in a nutshell" 
  3. ^ Chomskyan linguistics encourages the investigation of "corner cases" that stress the limits of its theoretical models comparable to pathological phenomena in mathematics, typically created using thought experiments, rather than the systematic investigation of typical phenomena that occur in real-world data, as is the case in corpus linguistics The creation and use of such corpora of real-world data is a fundamental part of machine-learning algorithms for NLP In addition, theoretical underpinnings of Chomskyan linguistics such as the so-called "poverty of the stimulus" argument entail that general learning algorithms, as are typically used in machine learning, cannot be successful in language processing As a result, the Chomskyan paradigm discouraged the application of such models to language processing
  4. ^ CoNLL
  5. ^ SIGNLL
  6. ^ Kishorjit, N, Vidya Raj RK, Nirmal Y, and Sivaji B 2012 "Manipuri Morpheme Identification", Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing SANLP,pages 95–108, COLING 2012, Mumbai, December 2012
  7. ^ Yucong Duan, Christophe Cruz 2011, Formalizing Semantic of Natural Language through Conceptualization from Existence International Journal of Innovation, Management and Technology2011 2 1, pp 37-42
  8. ^ "Versatile question answering systems: seeing in synthesis", Mittal et al, IJIIDS, 52, 119-142, 2011
  9. ^ Ciaramella, Alberto, Giancarlo Pirani, and Claudio Rullent "Conclusions and Future Developments" Advanced Algorithms and Architectures for Speech Understanding Springer Berlin Heidelberg, 1990 265-274
  10. ^ Roberto Pieraccini, Esther Levin, Chin-Hui Lee: Stochastic representation of conceptual structure in the ATIS task,, Proc Fourth Joint DARPA Speech and Natural Lang Workshop, Pacific Grove, CA, Feb 1991
  11. ^ Christopher D Manning, Hinrich Schütze: Foundations of Statistical Natural Language Processing, MIT Press 1999, ISBN 978-0-262-13360-9, p xxxi
  12. ^ Pirani, Giancarlo, ed Advanced algorithms and architectures for speech understanding Vol 1 Springer Science & Business Media, 2013
  13. ^ Magnini, B, Cappelli, A, Tamburini, F, Bosco, C, Mazzei, A, Lombardo, V, Bertagna, F, Toral, A, Bartalesi Lenzi, V, Sprugnoli, R & Speranza, M 2008, May Evaluation of Natural Language Tools for Italian: EVALITA 2007 In Proceedings of LREC 2008
  14. ^ Attardi, G, Basile, V, Bosco, C, Caselli, T, Dell’Orletta, F, Montemagni, S, Patti, V, Simi, M & Sprugnoli, R 2015 State of the Art Language Technologies for Italian: The EVALITA 2014 Perspective Intelligenza Artificiale, 91, 43-61

Further reading

  • Bates, M 1995 "Models of natural language understanding" Proceedings of the National Academy of Sciences of the United States of America 92 22: 9977–9982 doi:101073/pnas92229977 
  • Steven Bird, Ewan Klein, and Edward Loper 2009 Natural Language Processing with Python O'Reilly Media ISBN 978-0-596-51649-9
  • Daniel Jurafsky and James H Martin 2008 Speech and Language Processing, 2nd edition Pearson Prentice Hall ISBN 978-0-13-187321-6
  • Christopher D Manning, Prabhakar Raghavan, and Hinrich Schütze 2008 Introduction to Information Retrieval Cambridge University Press ISBN 978-0-521-86571-5 Official html and pdf versions available without charge
  • Christopher D Manning and Hinrich Schütze 1999 Foundations of Statistical Natural Language Processing The MIT Press ISBN 978-0-262-13360-9
  • David M W Powers and Christopher C R Turk 1989 Machine Learning of Natural Language Springer-Verlag ISBN 978-0-387-19557-5

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