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Natural language processing: state of the art, current trends and challenges

Diksha KhuranaAditya C KoliKiran KhatterSukhdev Singh

article2017Multimedia tools and applications1,568 citations

Presents a comprehensive historical and technical overview of natural language processing by breaking down its architectural levels, natural language generation components, modern real-world applications, and open research challenges.

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As digital textual information rapidly expands, organizations face substantial challenges in effectively interpreting, organizing, and utilizing human language computationally. Natural language processing bridges this gap by enabling computers to understand and generate human language, eliminating the need for human users to interact solely through complex programming syntax.

The article provides a broad review of the field, examining its foundational linguistic levels, historical evolution, algorithmic approaches, core applications, and recent commercial deployments.

The review evaluates established and emerging technologies across multiple domains. It traces historical milestones from early machine translation in the late 1940s to contemporary statistical and unsupervised machine learning methods, categorizing these tools into Natural Language Understanding and Natural Language Generation.

The key findings highlight that human language analysis spans seven interdependent linguistic levels: phonology, morphology, lexical analysis, syntax, semantics, discourse, and pragmatics. Processing methods have shifted from rule-based symbolic frameworks toward statistical, machine-learning, and deep neural approaches, with established methods like phrase chunking reaching high benchmark accuracy around 94.3% F1 score. Furthermore, practical implementations now span diverse sectors, including machine translation, automated document summarization, spam classification, medical record structuring, and enterprise dialogue systems.

These findings demonstrate that automated language systems can dramatically cut operational costs and labor requirements by automating high-volume document indexing, data classification, and regulatory compliance. Moreover, modern text-processing pipelines improve response timelines and user access across customer support, healthcare informatics, and cross-border communications, though ambiguity across syntax and informal web text remains an operational risk.

Organizations should adopt modular pipelines to allow flexible updates as component technologies improve, while pairing statistical methods with domain-specific knowledge to manage linguistic ambiguities. Enterprise teams must also evaluate privacy trade-offs when selecting conversational interfaces, as seen in proprietary messaging channels avoiding broad third-party platform access.

The article is a broad qualitative literature review rather than a single empirical benchmark study. Consequently, readers should exercise caution regarding specific performance claims in noisy real-world environments, as informal language, multi-language mixing, and cross-sentence discourse still require further development and evaluation.

Accendere Knowledge Management Services Pvt. Ltd.Manav Rachna International Universityemailinformation extractionmachine translationsnatural language processingcomputational linguisticsinformation extraction
arXiv: 1708.05148
Cover for Natural language processing: state of the art, current trends and challenges

Abstract

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

Topics

emailinformation extractionmachine translationsnatural language generationquestion answeringspamnatural language understandingtext processingnamed entity recognitionsyntactic parsing

Keywords

natural language processingcomputational linguisticsinformation extractionsemantic role labelingtext classificationword sense disambiguationmachine translationdialog systems

Table of Contents

  • 1. Introduction
  • 2. Levels of NLP
  • 1. Phonology
  • 2. Morphology
  • 3. Natural Language Generation
  • 4. History of NLP
  • 6. Applications of NLP
  • 6.1 Machine Translation
  • 6.2 Text Categorization
  • 6.3 Spam Filtering
  • 6.4 Information Extraction
  • 7. Approaches
  • 7.1 Hidden Markov Model (HMM)
  • 7.2 Naive Bayes Classifiers
  • 8. NLP in Talk
  • 8.1 ACE Powered GDPR Robot Launched by RAVN Systems [104]
  • 8.2 Eno A Natural Language Chatbot Launched by Capital One [105]
  • 8.3 Future of BI in Natural Language Processing [106]
  • 8.4 Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research [107]
  • 8.5 Meet the Pilot, world's first language translating earbuds [108]
  • References

Knowls

  1. Knowl 1 — Hierarchical Levels of Linguistic Analysis in Natural Language Processing

    model/method

    Natural language processing can be structured across seven hierarchical levels of linguistic analysis to represent, interpret, or generate human language:

    1. Phonology: The systematic arrangement and study of speech sounds used to encode meaning in spoken language.
    2. Morphology: The study of word formation and internal word structure by decomposing words into morphemes (the smallest meaningful linguistic units).
    3. Lexical Analysis: The interpretation of individual words, including assigning part-of-speech (POS) tags to each word based on local context and representing word-level senses.
    4. Syntactic Analysis: The examination of sentence structure using formal grammars and parsers to uncover structural dependency relationships and grammatical hierarchy between words.
    5. Semantic Analysis: The determination of literal meaning through interactions among word senses in a sentence, including word sense disambiguation within context.
    6. Discourse Analysis: The processing of multi-sentence text units focusing on inter-sentential connections, coreference, and document-level organization.
    7. Pragmatic Analysis: The inference of intended meaning beyond literal text by integrating real-world knowledge, situational context, conversational goals, and speaker intentions.

  2. Knowl 2 — Three-Stage Architecture for Natural Language Generation

    model/method

    Natural Language Generation (NLG) transforms non-linguistic internal representations into fluent, meaningful natural language text across three primary computational phases:

    1. Content Planning (Macroplanning): Selects the relevant subset of information from a knowledge base based on communicative goals, domain models, and user models, and establishes the overall discourse structure.
    2. Sentence Planning (Microplanning): Aggregates selected information units into sentence-sized segments, determines linguistic expressions, and performs lexicalisation (the selection of specific words and phrases).
    3. Surface Realization: Applies morphosyntactic and grammatical rules to map the structured sentence plans into final written natural language text or synthesized speech output.

  3. Knowl 3 — Taxonomy of Morphemes in Morphological Analysis

    definition

    In natural language morphology, words are composed of morphemes, which represent the smallest discrete units of grammatical and semantic meaning:

    • Lexical Morpheme: An independent base word that carries standalone semantic meaning (e.g., "table", "chair") and cannot be divided further.
    • Grammatical Morpheme: A morpheme that provides grammatical modification or relationship when combined with lexical morphemes (e.g., "-ed", "-ing", "-est", "-ly", "-ful").
    • Bound Morpheme: A grammatical morpheme that cannot occur in isolation and must be attached to another morpheme (e.g., inflectional affixes denoting verb tense or pluralization).
    • Derivational Morpheme: An affix combined with a base word that creates a new lexeme or changes the grammatical category of the base word.

  4. Knowl 4 — Generative versus Discriminative Modeling Paradigms in Statistical NLP

    model/method

    Statistical machine learning methods for natural language processing are categorized into generative and discriminative frameworks:

    • Generative Models: Estimate the joint probability distribution P(X,Y)P(X, Y) over observed inputs XX and targets or latent states YY. They can generate synthetic observations and encode structural domain assumptions (e.g., Naive Bayes classifiers, Hidden Markov Models). However, incorporating many overlapping or non-independent features makes joint probability estimation difficult.
    • Discriminative Models: Directly estimate the conditional posterior probability distribution P(Y∣X)P(Y \mid X) or determine decision boundaries based on observations without modeling the distribution of XX. They allow flexible use of large sets of overlapping, correlated features (e.g., Logistic Regression, Conditional Random Fields, Support Vector Machines).

  5. Knowl 5 — Multivariate Bernoulli versus Multinomial Event Models for Text Classification

    model/method

    In naive Bayes text categorization over a fixed vocabulary V={w1,w2,…,w∣V∣}V = \{w_1, w_2, \dots, w_{|V|}\}, documents are represented using two different event models:

    • Multi-variate Bernoulli Model: Documents are represented as binary vectors d=(x1,x2,…,x∣V∣)d = (x_1, x_2, \dots, x_{|V|}), where xi∈{0,1}x_i \in \{0, 1\} denotes the presence or absence of vocabulary word wiw_i in the document. Word frequency and word position are ignored.
    • Multinomial Model: Documents are represented as sequences or bags of word tokens, capturing both the presence of vocabulary words and their exact frequency counts within the document.

  6. Knowl 6 — Discourse-Level NLP Subtasks: Anaphora Resolution and Text Structure Recognition

    definition

    Discourse-level natural language processing operates across multi-sentence texts through two key subtasks:

    • Anaphora Resolution: The computational task of identifying referentially dependent words (such as pronouns or definite noun phrases) and replacing or linking them with the specific real-world entities (antecedents) to which they refer in preceding sentences.
    • Discourse and Text Structure Recognition: The identification of rhetorical and functional relationships between component sentences in a text (e.g., background, elaboration, contrast), enabling a unified structural and semantic representation of the document as a whole.

  7. Knowl 7 — Modular Data-Centric Pipeline Architecture for Multilingual Event Extraction

    model/method

    Cross-lingual event extraction across multiple source languages can be implemented using a modular, data-centric pipeline architecture structured like UNIX pipes:

    • Individual modules receive standardized data inputs, perform a specific linguistic analysis or extraction task, and generate standardized outputs that serve as inputs to downstream modules.
    • The pipeline cascades from low-level NLP operations (tokenization, morphological analysis, POS tagging) to high-level semantic components (cross-lingual named entity linking, semantic role labeling, time normalization).
    • Independent language pipelines feed their extracted outputs into a centralized system to construct event-centric knowledge graphs detailing events, participants, locations, and temporal relationships.

Coverage note — Historical summaries of early dialogue systems (e.g., BASEBALL, LUNAR, SHRDLU) and brief reviews of external commercial products (RAVN GDPR robot, Capital One Eno chatbot, Pilot translation earbuds) were omitted as they represent external background rather than original technical contributions.

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Citation

MLA
Khurana, D., et al. “Natural Language Processing: State of the Art, Current Trends and Challenges”. Multimedia Tools and Applications, vol. 82, no. 3, 2022, pp. 3713–44, https://doi.org/10.1007/s11042-022-13428-4.
APA
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4
Chicago
Khurana, D., A. Koli, K. Khatter, and S. Singh. 2022. “Natural Language Processing: State of the Art, Current Trends and Challenges”. Multimedia Tools and Applications 82 (3): 3713–44. https://doi.org/10.1007/s11042-022-13428-4.
Harvard
Khurana, D. et al. (2022) “Natural language processing: state of the art, current trends and challenges”, Multimedia Tools and Applications, 82(3), pp. 3713–3744. Available at: https://doi.org/10.1007/s11042-022-13428-4.
Vancouver
1. Khurana D, Koli A, Khatter K, Singh S (2022) Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications 82:3713–3744

BibTeX

@article{Khurana_2022, title={Natural language processing: state of the art, current trends and challenges}, volume={82}, ISSN={1573-7721}, url={http://dx.doi.org/10.1007/s11042-022-13428-4}, DOI={10.1007/s11042-022-13428-4}, number={3}, journal={Multimedia Tools and Applications}, publisher={Springer Science and Business Media LLC}, author={Khurana, Diksha and Koli, Aditya and Khatter, Kiran and Singh, Sukhdev}, year={2022}, month=July, pages={3713–3744} }
Metadata:Crossref

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