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Persian Computational Linguistics and NLP
This companion provides an overview of current work in the areas of Persian Computational Linguistics (CL) and Natural Language Processing (NLP). It covers a great number of topics and describes most innovative works of distinct academics researching the Persian language. The target group are researchers from computer science, linguistics, translation, psychology, philosophy, and mathematics who are interested in this topic.
Multilinear Subspace Learning
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensi...
Machine Learning
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary progr...
Linguistics and Philosophy
This volume contains papers by a large number of influencial linguists, written as a tribute to the work of Rulon S. Wells. The volume is subdivided into sections on the Philosophy of Language, Phonology, Syntax, Historical and Typological Linguistics, and Diachronic and Synchronic Derivation.
Multiword expressions
Multiword expressions (MWEs) are a challenge for both the natural language applications and the linguistic theory because they often defy the application of the machinery developed for free combinations where the default is that the meaning of an utterance can be predicted from its structure. There is a rich body of primarily descriptive work on MWEs for many European languages but comparative work is little. The volume brings together MWE experts to explore the benefits of a multilingual perspective on MWEs. The ten contributions in this volume look at MWEs in Bulgarian, English, French, German, Maori, Modern Greek, Romanian, Serbian, and Spanish. They discuss prominent issues in MWE research such as classification of MWEs, their formal grammatical modeling, and the description of individual MWE types from the point of view of different theoretical frameworks, such as Dependency Grammar, Generative Grammar, Head-driven Phrase Structure Grammar, Lexical Functional Grammar, Lexicon Grammar.
Binominal Lexemes in Cross-Linguistic Perspective
The typological, contrastive, and descriptive studies in this volume investigate the strategies employed by the world’s languages to create complex denotations by combining two noun-like elements, together with the kinds of semantic relation they involve, and their acquisition by children. The term ‘binominal lexeme’ is employed to cover both noun-noun compounds and a range of other naming strategies, including prepositional compounds, relational compounds, construct forms, genitival constructions, and more. Overall, the volume suggests a new, cross-linguistic approach to the study of complex lexeme formation that cuts across the traditional boundaries between syntax, morphology, and lexicon.
A First Course in Machine Learning
A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (http://bit.ly/firstcourseml), an extensive collection of MATLAB®/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
Multiword expressions at length and in depth
The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual reso...
Multiword expressions in lexical resources
This volume contains chapters that paint the current landscape of the multiword expressions (MWE) representation in lexical resources, in view of their robust identification and computational processing. Both large-size general lexica and smaller MWE-centred ones are included, with special focus on the representation decisions and mechanisms that facilitate their usage in Natural Language Processing tasks. The presentations go beyond the morpho-syntactic description of MWEs, into their semantics. One challenge in representing MWEs in lexical resources is ensuring that the variability along with extra features required by the different types of MWEs can be captured efficiently. In this respect, recommendations for representing MWEs in mono- and multilingual computational lexicons have been proposed; these focus mainly on the syntactic and semantic properties of support verbs and noun compounds and their proper encoding thereof.
Sparse Modeling
Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The...
