Natural language processing to examine the mission statements of nonprofit brands: an empirics-first approach

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Natural Language Processing (NLP) is mainly concerned with the development of computational models and tools of aspects of human (natural) language processing. Part of Speech Tagging (POS) is well studied topic and also one of the most fundamental preprocessing steps for any language in NLP. Natural language processing of Nepali is still lack significant research efforts in the area of NLP in India. POS tagging of Nepali is a necessary component for most NLP applications in Nepali, which analyses the construction of the language, behavior of the language and can be used to develop automated tools for language processing. From the literature survey and related works, it has been found that, not much work has been done previously on POS tagging for Nepali language in India due to lack of comprehensive set of tagged corpus or correct hand written rules. In this paper, Hidden Markov Model (HMM) based Part of Speech (POS) tagging for Nepali language has been discussed. HMM is the most popular used statistical model for POS tagging that uses little amount of knowledge about the language, apart from contextual information of the language. The evaluation of the tagger has been done using the corpora, which are collected from TDIL (Technology Development for Indian Languages) and the BIS tagset of 42 tags. Tagset has been designed to meet the morph-syntactic requirements of the Nepali language. Apart from corpora and the tagset, python programming language and the NLTK's (Natural Language Toolkit) library has been used for implementation. The tagger achieves accuracy over 96% for known words but for unknown words, the research is still continuing.

For the effective organisation of educational processes supported by relevant intelligent learning systems, it is important to choose the right technologies that would ensure individualisation of learning, adequate perception of learning content, and the so-called “understanding” of texts in Ukrainian provided by students (description of the solution to a task, answers provided in their own words, not selected from the test answer options, questions to the system, etc.), prototyping, constant iteration during natural language text recognition and processing, and maximum reliability and efficiency of learning processes. The purpose of the article is to study and analyse various methods of natural language processing, and the concept of NLP, and to consider common problems and prospects for developing a software product for processing Ukrainian-language text in online courses that support intelligent learning systems based on it. The research methods are the main methodological approaches and technological tools for analysing natural language texts in intelligent educational systems and developing a system for supporting NLP (Natural Language Processing) technology in the linguistic analysis of texts in Ukrainian. Such methods include, in particular: systemic and comparative analyses to identify the features of intelligence and information (with elements of intellectualisation) systems; the method of expert evaluation, which involves the study of literary sources and information resources, interviews and surveys of experts, as well as the processes of developing and testing intelligent and information systems. The novelty of the study is the analysis of modern technologies for the development of online educational process support systems through the organisation of processes of perception of information provided by students in natural language, the results of which can be used in the development of their software product to support the educational process in Ukrainian, ensuring the improvement of learning efficiency through the use of NLP technology in the process of studying the relevant academic content. Conclusions. The paper analyses modern NLP methods. The analysis has led to the selection of tokenisation, normalisation, stemming and lemmatisation methods for use in intelligent learning systems in the linguistic analysis of the so-called “free” communication in the natural (Ukrainian) language of students in the process of studying the educational content of online courses. During the tokenisation of Ukrainian-language texts, we solved such problems as eliminating so-called “merged” tokens, correcting spelling mistakes, identifying common prefixes in compound words and their impact on the semantics of the corresponding lexemes, identifying common prefixes in abbreviations, and bringing words to their normal form. Lemmatisation is especially important for the Ukrainian language (with its large number of cases of nouns, adjectives, word forms, etc.) and it requires the use of specially compiled dictionaries of the subject area under consideration. In these dictionaries, word forms are presented in the forms of lemmas (i.e., nouns are presented in the nominative case).

Etat actuel de cette methode. Quelques ameliorations basees sur l'emploi du traitement du langage naturel ont atteint finalement les moteurs de recherche du commmerce, mais il n'y a pas d'usage normalise en terminologie pour decrire leur processus

Natural language querying for remote sensing databases means that the users describe the query object directly in natural language when they query remote sensing (RS) databases. This method is not limited by the options on the query interface, and allows non-professional users to access to the databases easily. After making a study on the status of natural language querying for databases, the paper sums up three methods used in natural query language processing, which are Pattern Matching, Dependency Extraction and Knowledge Extension. Then it proposes a new method for Natural Language Processing (NLP) based on the features of the RS query text, which establishes a prototype system that contains three main steps-keywords extraction, keywords extension and SQL generation. The system implements the conversion from natural query language to database manipulation language (e.g. SQL), which greatly facilitates the users' query.

The NLP is closer for interfacing among the peoples knowing different languages. If we consider an example of India there are various peoples talking in various languages. Huge literature is available in different local languages which is not understandable to others in India itself. So we can use Information technology for Natural Language Processing. Natural language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages; it began as a branch of artificial intelligence. In theory, natural language processing is a very attractive method of human-computer interaction. Natural language understanding is sometimes referred to as an AI-complete problem because it seems to require extensive knowledge about the outside world and the ability to manipulate it. Modern NLP algorithms are grounded in machine learning, especially statistical machine learning. Research into modern statistical NLP algorithms requires an understanding of a number of disparate fields, including linguistics, computer science, and statistics. In this paper we want to study on Role of NLP for Indian Language conversions, like Marathi to Hindi, Hindi to Gujarati etc. If we observe the different languages in India they look similar in different aspects like Grammar, Words, and Alphabets. This paper will discuss the solutions available, problems and challenges in Indian Language conversions.

Natural language processing involves computer science, artificial intelligence and computational linguistics concerned with interactions between computers and human (natural) languages. The paper attempts to critically analyse state of the art technology algorithms in the field of Information Extraction and Information Retrieval. Information Extraction is concerned in general with the extraction of semantic information from text. Retrieval, filtering, indexing and other such tools have been built which have been used to accomplish tasks such as named entity recognition, co-reference resolution, relationship extraction, etc. By collating important work systematically, the paper also aims to simplify the process of referencing and literature review for future researchers and developers in the field of Natural Language Processing. Major challenges in NLP including natural language understanding, enabling computers to derive meaning from human or natural language input; natural language generation among others have also been discussed. Keywords: Natural Language Processing, Information Extraction, Information Retrieval, Machine Translation, Natural Language Generation.

Event Abstract Back to Event The redundancy of recursion and infinity for natural language and arithmetic processing Erkki Luuk1* and Hendrik Luuk2 1 University of Düsseldorf, Germany 2 University of Tartu, Estonia An influential line of thought claims that natural language and arithmetic processing require recursion, a putative hallmark of human cognitive processing (Chomsky, 2010; Fitch et al., 2005; Hauser et al., 2002). We have analyzed the plausibility of this claim (Luuk & Luuk, 2011), and conclude with the following points. First, both recursion and iteration allow for finite definitions of infinite sets. Moreover, iterative solutions are frequently less resource demanding than recursive ones. Second, contrary to Chomsky et al. (Chomsky, 1995; Hauser et al., 2002), we argue that a property of natural language is not discrete infinity but physically uncountable finity. Third, we reject the received opinion, articulated by Chomsky and colleagues, that neurally implemented recursion is necessary to explain natural language and arithmetic competence and performance. The only motivation for neurally implemented recursion is infinity in natural language and arithmetic competence (e.g. the knowledge that one can add 1 to n, append a natural language expression to text or embed clauses indefinitely). We claim that infinity in natural language and arithmetic competence reduces to imagining infinite embedding or concatenation, which is completely independent from an algorithmic capacity for infinite processing, and hence, completely independent from neurally implemented recursion or iteration. As the only purported motivators for recursion in human cognitive processing are natural language and arithmetic, the need for recursion in human cognitive processing is effectively downplayed. In sum, we submit that there is no infinity in natural language and arithmetic processing, but even if there were, iteration would be sufficient for generating it. Keywords: Arithmetic processing, Language Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Neural Bases of Language Citation: Luuk E and Luuk H (2011). The redundancy of recursion and infinity for natural language and arithmetic processing. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00299 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Erkki Luuk, University of Düsseldorf, Düsseldorf, Germany, erkkil@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Erkki Luuk Hendrik Luuk Google Erkki Luuk Hendrik Luuk Google Scholar Erkki Luuk Hendrik Luuk PubMed Erkki Luuk Hendrik Luuk Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Most higher education institutions have a mission statement that is developed strategically by the institutions and often reflect the college's unique mission which sets it apart from peer institutions. Through this study, we describe the use of a Machine Learning and Natural Language Processing based textual data analytics to understand the word choices in the mission statements of U.S. based colleges of engineering. Our purpose is to understand the key similarities and differences between the choice of words used in the mission statements of the two groups: public colleges and private colleges of engineering. We were specifically interested in studying the terms related to diversity and inclusion and see the trends in the use of specific terms relating to diverse communities, intersections, minority populations, and the like. In this research study, we used a Word2Vec approach to visualize the words from mission statements for 59 colleges of engineering in the United States. The contribution of this research is in the form of a visualization mapping the vector space model for word usage and complete vocabulary of pertinent words from the statements analyzed. The preliminary results of this study will help inform current state of vocabulary used in mission statements in the colleges of engineering across the United States. Ultimately, such analyses can help administrators in the development of strategies on the formation of mission and vision statements for universities by allowing insight into vocabulary currently used, to understand what words/terms may not be adequately addressed. Additionally, we are excited to present a contemporary textual data analytical technique of Natural Language Processing using a word vector representation tool such as word2vec for analyzing textual data in the field of engineering education.

Natural Language Processing and Computational Linguistics

Natural language processing has got great progress recently. Controlling robots with spoken natural language has become expectable. With the reliability problem of this kind of control in mind, a confirmation process of natural language instruction should be included before carried out by the robot autonomously; and the prototype dialog system was designed, thus the standardization problem was raised for the natural and understandable language interaction. In the application background of remotely navigating a mobile robot inside a building with Chinese natural spoken language, considering that as an important navigation element in instructions a place name can be expressed with different lexical terms in spoken language, this paper proposes a model for substituting different alternatives of a place name with a standard one (called standardization). First a CRF (Conditional Random Fields) model is trained to label the term required be standardized, then a trained word embedding model is to represent lexical terms as digital vectors. In the vector space similarity of lexical terms is defined and used to find out the most similar one to the term picked out to be standardized. Experiments show that the method proposed works well and the dialog system responses to confirm the instructions are natural and understandable.

Natural language communication with computers has long been a major goal of Artificial Intelligence both for what it can tell us about intelligence in general and for its practical utility - data bases, software packages, and Al-based expert systems all require flexible interfaces to a growing community of users who are not able or do not wish to communicate with computers in formal, artificial command languages. Whereas many of the fundamental problems of general natural language processing (NLP) by machine remain to be solved, the area has matured in recent years to the point where practical natural language interfaces to software systems can be constructed in many restricted, but nevertheless useful, circumstances. This tutorial is intended to survey the current state of applied natural language processing by presenting computationally effective NLP techniques, by discussing the range of capabilities these techniques provide for NLP systems, an by discussing their current limitations. Following the introduction, this document is divided into two major sections: the first on language recognition strategies at the single sentence level, and the second on language processing issues that arise during interactive dialogues. In both cases, we concentrate on those aspects of the problem appropriate for interactive natural languagemore » interfaces, but relate the techniques and systems discussed to more general work on natural language, independent of application domain.« less

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

Natural language processing (NLP) is an area of research that is used to investigate the application of natural language and is the foundation of machine translation, natural language text processing, natural language generation, multilingual and cross language information retrieval, speech recognition, parsing, and expert systems. To understand natural language in order to build or select appropriate algorithms for processing, three major issues are called into attention: humans' thought processes, the meaning of linguistic input in context, and world knowledge. These considerations have led to the development of various types of NLP tools for lexical and morphological analysis, semantic and discourse analysis, as well as knowledge-based approaches (c.f., Chowdhury, 2003). After decades of evolution and advancement, the current stage of NLP, as Xi (2010) pointed out, has allowed language testing researchers to apply its techniques in developing automated scoring systems for the purpose of language learning and assessment. The algorithms of NLP provide automated scoring systems a solid theoretical ground. Automated scoring systems have been adopted mainly for two kinds of language assessments: writing (i.e., essay scoring) and speaking (i.e., speech scoring). Automated essay scoring systems are generally designed to identify examinees' written production features in terms of fluency (the number of words in the essay), diction (the variation in word length), and syntactic complexity (the number of various parts of speech). Several expert essay-scoring systems have been published, such as PEG (Project Essay Grade), IEA (Intelligent Essay Assessor), BETSY (Bayesian Essay Test Scoring sYstem), and IntelliMetric. Among all, the most well-known system is perhaps e-rater, developed by Educational Testing Service (ETS). The original intent of e-rater was for it to serve as a second rater in the Analytic Writing Assessment (AWA) in GMAT; currently, e-rater is also used as a second rater in the analytic writing section in GRE, the independent writing task in TOEFL iBT, and as the sole rater for TOEFL online practice tests. Interestingly, there have not been as many expert systems for automated speech scoring compared to those for automated essay scoring. To date, only two have been more commonly applied in language assessment: Versant Tests by Ordinate Corporation and SpeechRater, by Educational Testing Service (ETS). Versant Tests aim to assess examinees' everyday listening and speaking ability by computing their test scores in listening vocabulary, repeat accuracy, reciting and pronunciation, reading fluency, and repeat fluency. The scoring system also takes suprasegmental features (e.g., timing, pause, rhythm, etc) into account. According to Ordinate Corporation's internal research, Versant Tests allow for high level of test administration efficiency, and their test results yield high reliability as well as high predictability of examinees' performance in real life (Townshend & Todic, 1999). However, other researchers (c.f., Bernstein, 1999; Xi, Higgins, Zechner, & Williamson, 2008) have pointed out that the task types adopted in Versant Tests limit the representativeness of communicative competence since a lot of the higher-order cognitive abilities or complex linguistic knowledge are not present. SpeechRater, on the other hand, was developed by ETS specifically for scoring the speaking

Natural Language (NL) processing tools, such as tokenizers, part-of speech taggers or syntactic processors obtain knowledge from a set of documents (e.g., tokens, syntactic patterns, etc.) and produce the different elements that will take part on the discourse universe in a NL text (e.g., noun phrases, verbs, sentences, etc.). In this paper, we present how NL software systems development can be performed incrementally by using a high-performance specification language like Maude. A generic algebraic specification for NL is defined, including sorts and sub-sorts apart from equational properties, such as associativity and commutativity for built-in lists and sets. Then, the full discourse universe, available for NL processing, is described in terms of the algebraic specification by providing a non-deterministic but terminating set of transformation rules. Finally, and as a proof of concept, a set of documents for NL processing is given to Maude as an input term and successfully transformed into a proper document, exploring all the non-deterministic possibilities, as well as resolving the ambiguity in language. The main advantages of implementing NL in this manner are: generality, transparency, extensibility, reusability, and maintainability. To the best of our knowledge, this is the first attempt to represent and develop complex NL software systems with this formal notation, and based on the analysis conducted, this implementation constitute the basis for the design and development of more specific NL processing applications, such as text summarization.