Natural Language Processing: Between Theoretical and Computational Linguistics

Senses are the extensions of language and interrelations between word meanings within language and the basis of flexible language use. Computers are basically made to process symbols. Computational linguistics is to simulate part of, if not all of, the natural language competence. From the perspective of computational linguistics, this paper sheds some light on semantic formalization, and proposes tagging of senses along with tagging weight values when processing word meanings. Also, this paper makes attempts to apply sense weight values to sentence processing in computational linguistics. Keywords-semantics, sense, weight, formalization, computational linguistics I. SEMANTIC KNOWLEDGE IN LANGUAGE PROCESSING According to prototype theory, semantic knowledge is part of human’s cognitive and belief systems. Semantics is also the critical research area since its initiation in 1893 by Michael Breal, and then by Trier and Lakoff who contributes to semantic research from both the descriptive and generative perspective. In linguistic semantics, rules and principles explain how literal meaning is coded in language and how to understand the “senses” of an expression or sense-relations that hold between one word and other expressions within the same language (Ceusters et al, 1999). Language is the carrier of both thoughts and forms. Thoughts are to induce, deduce, infer and judge, while senses are the contents and basis of thoughts. Senses are not the repeated addition of forms, rather a psychological process. Semantics is the mapping of cognitive structure from language expressions. Based on concepts, which are the abstract and general categorization of objects, people can apply semantic principles, situational contexts and encyclopedic knowledge to construct cognitive schema of the outside world. With frequent interactions and cognitive strategies, limited forms can be used to express unlimited connotation of thoughts (Tesser and Leone, 1977). For example, same meanings can be expressed with different semantic forms. a) 他这个人真有意思 (funny); b) 于是人们以为他们有了意思 (wish); c) 你向他意思意思 (express)如何; d) .我根本没有那个意思 (thought); e) 他常说人生真没有意思 (nonsense); f) 你们这么说是什么意思 (intention) These sentences are easy to comprehend. People not only use the grammatical structures but also the sense weights to process the words. The sense weight schema are stored in memory, and activated to represent and distill other sense weights. In this chain activation, sense weights are restructured and created to reflect prototypes. For example, “你好?”, “你好么?”, “好么你?”. Different structures are used in the above examples, but their essential meaning remains the same. So, semantic knowledge is of utmost importance in language comprehension. II. THE STATUS QUO OF COMPUTATIONAL LINGUISTICS Three domains and three corresponding levels of study are involved in building natural language processing systems, i.e. theoretical linguistics and grammar level, computational linguistics and formalization level, computer technology and realization level. Among them, grammar level concerns the innate characteristics of language and paves the way for the latter modules. Realization level focuses on how to control computer operation and provides the efficient development tools and environment. Formalization level is the interface of grammar level and realization level, with the task to readjust the general grammar modules to form grammar modules for convenient computer processing. Computational linguists’ mission is to establish formal grammars (Kaufmann and Pfister, 2012). Computational linguistics is also to establish formalized mathematical modules to analyze and process natural language, and develop computer programs to make it possible that machines can simulate part of, if not all of, human behavior and competence. Computers can only be used to process symbols and all those processed by computers are symbolized with no exception of languages (Yushi, 2003). Meanwhile, there have been breakthroughs in processing Chinese characters and phrases, so sentence level processing has become critical. Basic researches in syntactic, semantic and pragmatic knowledge are the frontier studies in Chinese processing. And how to acquire syntactic and semantic knowledge is the key, while the latter constitutes the major difficulty in processing the word level senses. As aforementioned, semantic formalization can take on many impossible missions in artificial intelligence and computational linguistics, and face challenges at the same time. Thus, the researcher will use semantic field to investigate formalization issues. Semantic fields are the sememe systems, which include the semantemes to represent similarities and differences, and are classified by the subordinates, i.e. degree and number of abstraction of Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Published by Atlantis Press, Paris, France.

Preface Typographical conventions 1. Language, speech and writing 2. In defence of (so-called) autonomous linguistics 3. Linguistic theory and theoretical linguistics 4. Natural, non-natural and unnatural languages: English, Urdu and other abstractions 5. The origin of language, speech and languages 6. Phonemic and non-phonemic phonology: some typological reflections 7. Towards a 'notional' theory of the 'parts of speech' 8. Deixis as the source of reference 9. Deixis and anaphora Appendix: the scientific study of language. Inaugural lecture, Edinburgh, 1965 Notes References Subject index Names index.

Nowadays most of people of developed economy countries interact with software every day. As a result of computer systems expansion to all scope of people's activity the problem of transition from visual and command interfaces to natural language user interfaces is thrown into the sharp relief. Computational linguistics and natural language processing methods are described in this article. Represent a formal definition of a dialogue system as a Markov decision process. Methods of natural language manipulation are applied in machine translation software systems, systems of search and exchange the data, text annotation and expert systems. Prototype of natural language user interface to structured data source is developed. As a result it is convert natural language user's query to SQL query to database. Also showed application of Levenshtein distance for processing natural language query to SQL query to database. Natural language user interface is created to predefined subject field. User interface interacts to database that contain information about existent program libraries and frameworks. Consequently, using natural language processing methods it is possible to develop user natural language user interface providing capability to interact with machine.

This Delphi study investigates whether linguists from diverse theoretical backgrounds can reach consensus on core metaconcepts at the syntax–semantics interface, and how these metaconcepts are perceived as interconnected within linguistic theory and education. Expanding on a previous study conducted primarily with Dutch experts, this research draws on an international sample of 58 linguists across generative, cognitive and functional traditions. Through iterative Delphi rounds and Perceived Causal Network analysis, participants evaluated and refined a shared set of metaconcepts and their perceived relationships. The study identifies a stable core of foundational metaconcepts valued across theoretical traditions and shows that the perceived importance of 23 metaconcepts is largely reproduced from the earlier study, despite the broader linguistic and theoretical diversity of the current expert group. Structural metaconcepts continue to be rated as the most important ones for both theoretical linguistics and language education. The network analyses further illuminate how experts view interdependencies among key metaconcepts, revealing several that function as threshold concepts and may need to be acquired before others can be fully understood. These findings strengthen the validity of a metaconceptual approach to grammar teaching, in which the school grammar curriculum is enriched with metaconcepts that are relevant in linguistic theory. Ultimately, the study helps bridge divisions between theoretical schools of thought and between linguistics and grammar education, offering a shared and empirically grounded foundation for developing learners’ metalinguistic understanding.

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.

Researches in Computational Linguistics (CL) and Natural Language Processing (NLP) have been increasingly dissociated from each other. Empirical techniques in NLP show good performances in some tasks when large amount of data (with annotation) are available. However, in order for these techniques to be adapted easily to new text types or domains, or for similar techniques to be applied to more complex tasks such as text entailment than POS taggers, parsers, etc., rational understanding of language is required. Engineering techniques have to be underpinned by scientific understanding. In this paper, taking grammar in CL and parsing in NLP as an example, we will discuss how to re-integrate these two research disciplines. Research results of our group on parsing are presented to show how grammar in CL is used as the backbone of a parser.KeywordsNatural Language ProcessingLexical EntryDerivation TreeElementary TreeComputational LinguisticThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computational linguistic and natural language processing automation tasks require text annotated with tags that represent the desired output of the task. The annotation tags serve for training, validation, and evaluation. Arabic morphological analysis, and tags associated with it such as part of speech and gloss tags, is key to Arabic computational linguistics and natural language processing. Several manual and automated tagging tools exist for text. Very few exist that are based on Arabic morphological analysis. In this paper, we present an open source tagging tool with visual interface that enables the construction of annotated Arabic text corpora with automatic morphology-based tags. The tool allows the specification of tags with Boolean formulae where the atomic predicates are match and contain relations between the morphological solution of part of the text and the value of a morphological feature. The tool allows the user to directly enter manual tags, to edit existing tags through a tag sensitive coloring interface, to compare tag sets, and compute accuracy results.

The concept of polarity is pervasive in natural language. It relates syntax, semantics and pragmatics narrowly (Giannakidou, in: Maienborn, von Heusinger, Portner (eds.), Semantics: an international handbook of natural language meaning, De Gruyter Mouton, Berlin, 2011; Israel in The grammar of polarity: pragmatics, sensitivity, and the logic of scales, Cambridge studies in linguistics, Cambridge University Press, Cambridge, 2014), it refers to items of many syntactic categories such as nouns, verbs and adverbs. Neutral polarity items appear in affirmative and negative sentences, negative polarity items cannot appear in affirmative sentences, and positive polarity items cannot appear in negative sentences. A way of reasoning in Natural Language is through Natural Logic (van Benthem in Essays in logical semantics, vol. 29 of Studies in linguistics and philosophy, Reidel, Dordrecht, 1986; Language in action: categories, lambdas, and dynamic logic, vol. 130 of Studies in logic, Elsevier, Amsterdam, 1991). This logic is based on the concept of polarity in order to make the meaning of a sentence weaker o stronger without changing its truth value. There exist many proposals to compute polarity in the Natural Logic context, the most widely known are the ones by: van Benthem (1986, 1991), Sanchez-Valencia (Studies on natural logic and categorial grammar, Ph.D. thesis, Universiteit van Amsterdam, 1991), Dowty (Proceedings of the 4th conference on semantics and theoretical linguistics, Cornel University, CLC Publications, Rochester, 1994), and van Eijck (in: ten Cate, Zeevat (eds.), 6th international Tbilisi symposium on logic, language, and computation, Batumi, Georgia, Springer, 2007). If Natural Logic is going to be used, as an inferential mechanism between text fragments, in Natural Language Processing applications such as text summarization, question answering, and information extraction, it is a priority to know what the existing relationship among the aforementioned algorithms is; for example, to implement the most general. We show in this paper the equivalence among the analyzed algorithms, filling a gap in Natural Logic research, particularly in computing polarity, and the soundness of their algorithms.

Introduction. Donald Walker: a Remembrance. Section 1: The Task of Natural Language Processing. Natural Language Processing: an Historical Review K. Sparck Jones. On Getting a Computer to Listen J. Robinson. Utterance and Objective: Issues in Natural Language Communication B. Grosz. On the Proper Place of Semantics in Machine Translation M. King. Developing a Natural Language Interface to Complex Data G.G. Hendrix, E.D. Sacerdoti. User-Needs Analysis and Design Methodology for an Automated Document Generator K. Kukich, K. McKeown, J. Shaw, J. Robin, J. Lim, N. Morgan, J. Philips. Section 2: Building Computational Lexicons. Machine-Readable Dictionaries and Computational Linguistics Research B. Boguraev. Research Toward the Development of a Lexical Knowledge Base for Natural Language Processing R.A. Amsler. Discovering Relationships Among Word Senses R.J. Byrd. Machine Readable Dictionary as a Source of Grammatical Information E. Hajicova, A. Rosen. The ITT Lexical Database: Dream and Reality S. Pin-Ngern Conlon. Visions of the Digital Library: Views on Using Computational Linguistics and Semantic Nets in Information Retrieval J.L. Klavans. Anatomy of a Verb Entry: from Linguistic Theory to Lexicographic Practice B.T. Atkins, J. Kegl, B. Levin. Issues for Lexicon Building N. Calzolari. Outline of a Model for Lexical Databases N. Ide, J. le Maitre, J. Veronts. Construction-Based MT Lexicons L. Levin, S. Nirenburg. Dependency-Based Grammatical Information in the Lexicon P. Sgall. Semantics in the Brain's Lexicon -- Some Preliminary Remarks on its Epistemology H. Schnelle. Section 3: The Acquisition and Use of LargeCorpora. The Ecology of Language D.E. Walker. Representativeness in Corpus Design D. Biber. The Text Encoding Initiative C.M. Sperberg-McQueen. Discrimination Decisions for 100,000 Dimensional W.A. Gale, K.W. Church, D. Yarowsky. Acquisition and Exploitation of Textual Resources for NLP S. Armstrong-Warwick. The Center for Electronic Texts in the Humanities S. Hockey. Design Principles for Electronic Textual Resources: Investigating Users and Uses of Scholarly Information N.J. Belkin. Section 4: Topics, Methods and Formalisms in Syntax, Semantics and Pragmatics. Evaluating English Sentences in a Logical Model J. Friedman, D.B. Moran, D.S. Warren. Recovering Implicit Information M.S. Palmer, D.A. Dahl, R.J. Schiffman, L. Hirschman, M. Linebarger, J. Downing. Flexible Generation: Taking the User into Account C.L. Paris, V.O. Mittal. Two Principles of Parse Preference J.R. Hobbs, J. Bear. UD, yet Another Unification Device R. Johnson, M. Rosner. Varieties of Heuristics in Sentence Parsing M. Nagao. Some Recent Trends in Natural Language Processing A.K. Joshi. Stone Soup and the French Room Y. Wilks.

Chapter 11 - Partiality

Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. These include spoken language systems that integrate speech and natural language; cooperative interfaces to databases and knowledge bases that model aspects of human-human interaction; multilingual interfaces; machine translation; and message-understanding systems, among others. Research in NLP is highly interdisciplinary, involving concepts in computer science, linguistics, logic, and psychology. NLP has a special role in computer science because many aspects of the field deal with linguistic features of computation and NLP seeks to model language computationally.

Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. These include spoken language systems that integrate speech and natural language; cooperative interfaces to databases and knowledge bases that model aspects of human-human interaction; multilingual interfaces; machine translation; and message-understanding systems, among others. Research in NLP is highly interdisciplinary, involving concepts in computer science, linguistics, logic, and psychology. NLP has a special role in computer science because many aspects of the field deal with linguistic features ofcomputation and NLP seeks to model language computationally.

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

This study explores semantic gradience in natural language by employing fuzzy-set theory to quantitatively to model the continuum of meaning inherent in emotional adjectives. Focusing on the term “happy”, we conducted an experimental case study with 30 participants who rated the word on a 7 -point Likert scale. The raw ratings were normalized to a interval, yielding a mean normalized rating of approximately 0.617 and a standard deviation of about 0.228. A Gaussian fuzzy membership function was derived using these empirical parameters, which effectively captures the smooth transition of membership degrees and the inherent fuzzy boundaries in semantic interpretation. The findings suggest that the meaning of “happy" isn’t simply a binary result; instead, there is a range of degrees of semantic similarity, with vast areas of overlap that conventional categorization approaches don't account for. This quantitative perspective leads to an interface between cognitive-functional linguistics and fuzzy mathematics, which thoroughly expands the insights into linguistic variation. The findings show that fuzzy set theory can enhance the theoretical models used to explain semantics in linguistics and provide useful insights for practitioners in the fields of computational linguistics and natural language processing. Further research extending this methodology to other semantic domains and including more diverse participant samples is warranted to further validate and refine the model.

Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning