Ruslan Mitkov

Bio: Ruslan Mitkov is an academic researcher from University of Wolverhampton. The author has contributed to research in topics: Machine translation & Anaphora (linguistics). The author has an hindex of 30, co-authored 164 publications receiving 3651 citations. Previous affiliations of Ruslan Mitkov include National Board of Medical Examiners & University of Seville.

The Oxford handbook of computational linguistics

Abstract: This collection of invited papers covers a lot of ground in its nearly 800 pages, so any review of reasonable length will necessarily be selective. However, there are a number of features that make the book as a whole a comparatively easy and thoroughly rewarding read. Multiauthor compendia of this kind are often disjointed, with very little uniformity from chapter to chapter in terms of breadth, depth, and format. Such is not the case here. Breadth and depth of treatment are surprisingly consistent, with coherent formats that often include both a little history of the field and some thoughts about the future. The volume has a very logical structure in which the chapters flow and follow on from each other in an orderly fashion. There are also many cross-references between chapters, which allow the authors to build upon the foundation of one another's work and eliminate redundancies. Specifically, the contents consist of 38 survey papers grouped into three parts: Fundamentals; Processes, Methods, and Resources; and Applications. Taken together, they provide both a comprehensive introduction to the field and a useful reference volume. In addition to the usual author and subject matter indices, there is a substantial glossary that students will find invaluable. Each chapter ends with a bibliography, together with tips for further reading and mention of other resources, such as conferences , workshops, and URLs. Part I covers the full spectrum of linguistic levels of analysis from a largely theoretical point of view, including phonology, morphology, lexicography, syntax, semantics, discourse, and dialogue. The result is a layered approach to the subject matter that allows each new level to take the previous level for granted. However, the authors do not typically restrict themselves to linguistic theory. For example, Hanks's chapter on lexicography characterizes the deficiencies of both hand-built and corpus-based dictionaries , as well as discussing other practical problems, such as how to link meaning and use. The phonology and morphology chapters provide fine introductions to these topics, which tend to receive short shrift in many NLP and AI texts. Part I ends with two chapters, one on formal grammars and one on complexity, which round out the computational aspect. This is an excellent pairing, with Martín-Vide's thorough treatment of regular and context-free languages leading into Carpen-ter's masterly survey of problem complexity and practical efficiency. Part II is more task based, with a focus on such activities as text segmentation, …

Robust Pronoun Resolution with Limited Knowledge

Abstract: Most traditional approaches to anaphora resolution rely heavily on linguistic and domain knowledge. One of the disadvantages of developing a knowledge-based system, however, is that it is a very labour-intensive and time-consuming task. This paper presents a robust, knowledge-poor approach to resolving pronouns in technical manuals, which operates on texts pre-processed by a part-of-speech tagger. Input is checked against agreement and for a number of antecedent indicators. Candidates are assigned scores by each indicator and the candidate with the highest score is returned as the antecedent. Evaluation reports a success rate of 89.7% which is better than the success rates of the approaches selected for comparison and tested on the same data. In addition, preliminary experiments show that the approach can be successfully adapted for other languages with minimum modifications.

Computer-aided generation of multiple-choice tests

Abstract: This paper describes a novel computer-aided procedure for generating multiple-choice tests from electronic instructional documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the program makes use of language resources such as a corpus and WordNet. The system generates test questions and distractors, offering the user the option to post-edit the test items.

A computer-aided environment for generating multiple-choice test items

Abstract: This paper describes a novel computer-aided procedure for generating multiple-choice test items from electronic documents. In addition to employing various Natural Language Processing techniques, including shallow parsing, automatic term extraction, sentence transformation and computing of semantic distance, the system makes use of language resources such as corpora and ontologies. It identifies important concepts in the text and generates questions about these concepts as well as multiple-choice distractors, offering the user the option to post-edit the test items by means of a user-friendly interface. In assisting test developers to produce items in a fast and expedient manner without compromising quality, the tool saves both time and production costs.

Anaphora resolution: the state of the art

Abstract: The etymology of the term "anaphora" goes back to Ancient Greek with “anaphora” (αναφορα) being a compound word consisting of the separate words ανα − back, upstream, back in an upward direction and φορα the act of carrying and denoted the act of carrying back upstream. For Computational Linguists embarking upon research in the field of anaphor resolution, I strongly recommend as a primer Graham Hirst's book "Anaphora in natural language understanding" (Hirst 1981) which may seem a bit dated in that it does not include developments in the 80's and the 90's, but which provides an excellent survey of the theoretical work on anaphora and of the early computational approaches and is still very useful reading.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A machine learning approach to coreference resolution of noun phrases

Abstract: In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of "organization," "person," or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, in-dicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.

「会話の文法」に関する一考察 : Longman Grammar of Spoken and Written Englishの場合

Abstract: Longman Student Grammar of Spoken and Written English March 13th, 2019 These tell us what choices are available in the grammar but we also need to understand how these choices are used to create discourse in different situations The year 1999 saw the publication of a large scale grammar of English with the aim of meeting the above needs the Longman ielts house net, longman student grammar of spoken and written english, longman grammar of spoken and written english roffel, longman student grammar of spoken and written english pdf, longman grammar of spoken and written english libros, longmans student grammar of spoken and written english, english longman grammar of spoken and written eng free, longman student grammar of spoken and written english, longman grammar of spoken and written english pdf web, lms2 vu edu pk, longman student grammar of spoken and written english, longman grammar of spoken and written english wikipedia, longman student grammar of spoken and written english, download pdf longman grammar of spoken and written, longman student grammar of spoken and written english, longman grammar of spoken and written english amazon co, longman student grammar of spoken and written english, longman grammar of spoken and written english edoc pub, the languagelab library longman student grammar of, longman grammar of spoken and written english scribd, longman grammar of spoken and written english free, the longman grammar of spoken and written english, longman grammar of spoken and written english epdf tips, grammars of spoken english new outcomes of corpus, longman grammar of spoken and written english tesl ej, book reviews longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english worldcat org, douglas biber et al longman grammar of spoken and, project muse longman grammar of spoken and written, longman grammar of spoken and written english oxford, 9780582237261 longman student grammar of spoken and, longman student grammar of spoken and written english, pdf longman grammar of spoken and written english, longman student grammar of spoken and written english, longman grammar of spoken and written english google books, student grammar of spoken and written english workbook, longman grammar of spoken and written english goodreads, longman student grammar of spoken and written english, longman student grammar of spoken and written english le, longman student grammar of spoken and written english, longman grammar of spoken and written english co construction, longman student grammar of spoken and written english, longman student grammar of spoken and written english by, longman student grammar of spoken and written english workbook, longman grammar of spoken and written english douglas

MaltParser: A language-independent system for data-driven dependency parsing

Abstract: Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.