Language Identification: Fundamentals and Applications

By Fouad Sabry

What Is Language Identification

The difficulty of determining which natural language a piece of information is written in is referred to as language identification or language guessing in the field of natural language processing. The computational approaches to this issue regard it as a particular instance of text categorization, which can then be solved using a variety of statistical techniques.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Language Identification

Chapter 2: Computational Linguistics

Chapter 3: Natural Language Processing

Chapter 4: Word-sense Disambiguation

Chapter 5: Cognitive Linguistics

Chapter 6: Part-of-speech Tagging

Chapter 7: N-gram

Chapter 8: Language Model

Chapter 9: Native-language Identification

Chapter 10: Word2vec

(II) Answering the public top questions about language identification.

(III) Real world examples for the usage of language identification in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of language identification' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of language identification.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 5, 2023

Book preview

Chapter 1: Language identification

The task of recognizing which natural language any given information is written in is known as language identification or language guessing in the field of natural language processing. Computational methods see this issue as a variant of text classification, which may be addressed using a number of different statistical techniques.

Several statistical methods exist for identifying languages, each using a unique method of data classification. One method involves comparing the text's compressibility to that of texts in a pool of existing languages. The term mutual information based distance measure is used to describe this method. Empirically constructing family trees of languages using the same techniques yields results that are very consistent with those obtained using historical methods. The mutual information distance measure is not widely regarded as unique or superior to more straightforward procedures, and it is fundamentally equal to more standard model-based methods.

A second method involves developing a linguistic n-gram model using a training text for each of the languages, as detailed by Cavnar and Trenkle (1994) and Dunning (1994). Character-based models (Cavnar and Trenkle) and encoded-byte models (Dunning) are also possible; the latter integrates the processes of identifying the language and determining the character encoding. A comparable model is then created for each piece of text that has to be detected, and this model is compared to each language model in the database. Whichever language's model is closest to the model extracted from the target text is the most probable candidate. When the input text is in a language without a model, this method might be difficult. In such instance, the procedure may provide a result in a different, most comparable language. Multilingual input text, commonplace on the Internet, presents a challenge for any method.

For a cutting-edge approach,, see Řehůřek and Kolkus (2009).

While n-gram techniques have difficulty with short texts with just a few words, this method can reliably recognize many languages within an unstructured piece of text.

Using the frequency of certain function words, Grefenstette developed an earlier statistical technique (e.g., the in English).

The use of common letter combinations, distinguishing diacritics, or unusual punctuation marks is a typical non-statistical intuitive technique (albeit fraught with doubt).

Distinguishing between closely related languages is a major challenge for language identification systems. There is a lot of lexical and structural overlap between closely related languages like Bulgarian and Macedonian or Indonesian and Malay, making it difficult for systems to tell them apart.

Group A (Bosnian, Croatian, and Serbian), Group B (Indonesian, and Malaysian), Group C (Czech, and Slovak), Group D (Brazilian Portuguese, European Portuguese), Group E (Peninsular Spanish, Argentine Spanish), and Group F (Swedish, and Norwegian) make up the 13 languages (and language varieties) in the DSL shared task dataset (Tan et al., 2014). (American English, British English). Over 95% accuracy was achieved by the top system (Goutte et al., 2014). The DSL shared task's outcomes are detailed in Zampieri et al. (2014).

In addition to a model that can differentiate between 103 languages, Apache OpenNLP also has a char n-gram based statistical detector.

The language detector in Apache Tika supports 18 different tongues.

{End Chapter 1}

Chapter 2: Computational linguistics

An interdisciplinary discipline, computational linguistics focuses on the computer modeling of natural language, as well as the investigation of relevant computational methods to various linguistic challenges. In general, computational linguistics draws on a wide variety of fields, including but not limited to linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, ethnography, and neuroscience.

In the past, computational linguistics developed as a subfield of artificial intelligence carried out by computer scientists who had specialized in the use of computers in the translation and analysis of natural languages. During the 1970s and 1980s, the subject was able to become more established thanks to the introduction of independent conference series as well as the foundation of the Association for Computational Linguistics (ACL).

The Association for Computational Linguistics (ACL) provides the following definition for the field of computational linguistics::

...the use of scientific methods and computer analysis to the study of language. Researchers in the field of computational linguistics are interested in developing computer models of many different types of language processes.

The terms natural language processing (NLP) and (human) language technology are increasingly being seen as being almost synonymous with the word computational linguistics. This is the case in the year 2020. Since the beginning of the 2000s, these phrases have placed more of a focus on the investigation of real applications rather than theoretical concepts. Although they solely pertain to the subfield of applied computational linguistics, in practice, they have largely supplanted the term computational linguistics in the NLP/ACL community. This is because they refer more explicitly to the subject of applied computational linguistics.

The study of computational linguistics incorporates both theoretical and practical aspects. The field of theoretical computational linguistics focuses on problems that arise in the fields of cognitive science and theoretical linguistics.

The creation of formal theories of grammar (parsing) and semantics is an important part of theoretical computational linguistics. These theories are often rooted in formal logics and symbolic (knowledge-based) techniques. Research domains that are within the purview of theoretical computational linguistics include the following::

The computational difficulty of natural language, which is based mostly on automata theory and makes use of context-sensitive grammar and linearly bounded Turing machines.

Determining appropriate logics for the encoding of linguistic meaning, automatically creating such logics, and reasoning with those logics are all components of computational semantics.

Machine learning, which has typically relied on statistical approaches and, from the middle of the 2010s, neural networks: Socher et al., is the most important aspect of applied computational linguistics (2012)

Other divisions of computational into main fields according to various criteria exist, such as the divide that exists between theoretical and practical computational linguistics. These divisions of computational include::

regardless of the spoken or written form of the language that is being processed: The fields of voice recognition and speech synthesis investigate how computers can comprehend spoken language and construct their own versions of it.

job that is being carried out, such as analyzing language (which involves recognition) or generating language (which involves generation): The subfields of computational linguistics that deal with disassembling and reassembling language are called parsing and generation, respectively.

Traditionally, the use of computers to solve research issues in areas of linguistics that fall under the purview of other subfields has been categorized as being within the purview of the field of computational linguistics. This involves a number of things, amongst others.