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Automatic Language Identification in Texts: A Survey

Journal of Artificial Intelligence Research

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature.


A Survey of Code-switched Speech and Language Processing

arXiv.org Machine Learning

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.



Rus

AAAI Conferences

Paraphrase identification is a core Natural Language Processing task that involves assessing the semantic similarity of two texts. To foster systematic studies of this task, standardized datasets were created on which various approaches could be compared more fairly. However, a better understanding and more precise operational definition of a paraphrase are needed before any further datasets or systematic evaluations of the task of paraphrase identification are proposed. This study develops the concept of paraphrasing as a writing strategy. Six types of paraphrases are defined through the creation of a relatively large corpus of student-generated paraphrases. These paraphrases are analyzed along several dozen linguistic dimensions ranging from cohesion to lexical diversity. The most significant indices from these dimensions were then used to build a prediction model that could identify true and false paraphrases and each of the six paraphrase types.


Identification of Pleonastic It Using the Web

Journal of Artificial Intelligence Research

In a significant minority of cases, certain pronouns, especially the pronoun it, can be used without referring to any specific entity. This phenomenon of pleonastic pronoun usage poses serious problems for systems aiming at even a shallow understanding of natural language texts. In this paper, a novel approach is proposed to identify such uses of it: the extrapositional cases are identified using a series of queries against the web, and the cleft cases are identified using a simple set of syntactic rules. The system is evaluated with four sets of news articles containing 679 extrapositional cases as well as 78 cleft constructs. The identification results are comparable to those obtained by human efforts.