CAML Model for Computational Linguistics

Cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words.

The present paper intends to draw the conception of language implied in the technique of word embeddings that supported the recent development of deep neural network models in computational linguistics. After a preliminary presentation of the basic functioning of elementary artificial neural networks, we introduce the motivations and capabilities of word embeddings through one of its pioneering models, word2vec. To assess the remarkable results of the latter, we inspect the nature of its underlying mechanisms, which have been characterized as the implicit factorization of a word-context matrix. We then discuss the ordinary association of the “distributional hypothesis” with a “use theory of meaning,” often justifying the theoretical basis of word embeddings, and contrast them to the theory of meaning stemming from those mechanisms through the lens of matrix models (such as vector space models and distributional semantic models). Finally, we trace back the principles of their possible consistency through Harris’s original distributionalism up to the structuralist conception of language of Saussure and Hjelmslev. Other than giving access to the technical literature and state of the art in the field of natural language processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophical stakes involved in the recent application of new neural network techniques to the computational treatment of language.

We report here the main results obtained in the theory of configurations, which is one of the most important theories in the field of analytical models of algebraic linguistics. Very interesting views on the status of the mathematical (algebraic) linguistics arepresented, among others, in [7, 12, 22, 23, 24, 25, 33, 47 and 48].

We present a new operational model for Computational Linguistics, based on Logic Programming Debugging, called here Restless Debugging. We also try to clarify a number of implications this model has for Knowledge Representation.

This paper presents a case study of discovering and classifying verbs in large web-corpora. Many tasks in natural language processing require corpora containing billions of words, and with such volumes of data co-occurrence extraction becomes one of the performance bottlenecks in the Vector Space Models of computational linguistics. We propose a co-occurrence extraction kernel based on ternary trees as an alternative (or a complimentary stage) to conventional map-reduce based approach, this kernel achieves an order of magnitude improvement in memory footprint and processing speed. Our classifier successfully and efficiently identified verbs in a 1.2-billion words untagged corpus of Russian fiction and distinguished between their two aspectual classes. The model proved efficient even for low-frequency vocabulary, including nonce verbs and neologisms.

The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a “transducer composition” algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple sequences related by a phylogenetic tree. This algorithm draws on formal models of computational linguistics as well as the 1985 protosequence algorithm of David Sankoff. The output of the composition algorithm is a multiple-sequence stochastic context-free grammar. We describe dynamic programming algorithms, which are robust to null cycles and empty bifurcations, for parsing this grammar. Example applications include structural alignment of non-coding RNAs, propagation of structural information from an experimentally-characterized sequence to its homologs, and inference of the ancestral structure of a set of diverged RNAs. We implemented the above algorithms for a simple model of pairwise RNA structural evolution; in particular, the algorithms for maximum likelihood (ML) alignment of three known RNA structures and a known phylogeny and inference of the common ancestral structure. We compared this ML algorithm to a variety of related, but simpler, techniques, including ML alignment algorithms for simpler models that omitted various aspects of the full model and also a posterior-decoding alignment algorithm for one of the simpler models. In our tests, incorporation of basepair structure was the most important factor for accurate alignment inference; appropriate use of posterior-decoding was next; and fine details of the model were least important. Posterior-decoding heuristics can be substantially faster than exact phylogenetic inference, so this motivates the use of sum-over-pairs heuristics where possible (and approximate sum-over-pairs). For more exact probabilistic inference, we discuss the use of transducer composition for ML (or MCMC) inference on phylogenies, including possible ways to make the core operations tractable.

The article discusses the stages of development of grammar and related phenomena, the creation of an electronic database of linguistic units and its inclusion in the National Corps of Languages, improving the quality of automatic translation in computer linguistics, linguistic modeling of language, the theory of lemma.

The monograph presents the Meaning-Text approach applied to the domain of syntax from a typological angle; it deals with several long-standing syntactic problems on the basis of a dependency description. Its content can be presented in five parts + an Introduction: The Introduction explains the architecture of the book and sketches the Meaning-Text linguis-tic model, underlying the subsequent discussion. I. Surface-syntactic relations in the languages of the world, with special studies of subjects and objects. II. Grammatical voice in the dependency framework: the “passive” construction in Chinese. III. The relative clause: a calculus and analysis of possible types; the pseudo-relative (“headless”) clause. IV. Binary conjunctions (such as IF …, THEN …), free indefinite pronouns ([He went] nobody knows where), and syntactic idioms. V. Word order: linearization of dependency structures. The monograph offers a new perspective in syntactic studies. It is strongly typology-oriented (using the data from typologically diverse languages: English, Russian, Chinese, Korean, Basque, Georgian, etc.) and based on a system of rigorous definitions of the notions involved, which ensures a link with computational linguistics and Natural Language Processing

How to build an effective language model in NLP field to capture the statistical laws of language, improve the performance of machine translation, and enable machines to understand and generate texts with complex semantic structures is still a problem that cannot be ignored. Firstly, word segmentation, cleaning, etc., and word embedding using Word2Vec pre-training model are carried out. Then the Transformer model is constructed, and the whole sequence is processed simultaneously through the self-attention mechanism. In this paper, the learning rate, the size of hidden layer and the number of layers are adjusted by grid search, and the dropout technology is used to prevent over-fitting. Transformer model performs well in machine translation tasks, with accuracy exceeding 95%, and the METEOR value is generally higher than that of LSTM model, which is stable at a high level. In 20 different text generation tasks, the BERT score of the model is between 0.83 and 0.99, which shows that the model can understand and generate complex semantic structures. These results prove the effectiveness of Transformer model in dealing with natural language tasks, and provide new directions and ideas for future research and application.

This paper presents a proof-of-concept implementation of an artificial intelligence system designed for automated generation of Python source code annotations. We demonstrate the feasibility of developing effective linguistic models with constrained computational resources, capable of producing usable documentation. The insights gained from evaluating these generated descriptions serve as a crucial feedback mechanism, guiding the iterative refinement of model capabilities and identifying niches where rapid deployment is prioritized over maximal precision. This work underscores advancements in practical computational linguistics for enhancing developer efficiency and streamlining software development lifecycles within dynamic enterprise contexts, a key area for modern Computer Science applications.