Language Parsing Research Articles

Los modelos científicos como herramientas epistémicasabstractas para aprender a razonar

The variety of scientific methodologies aimed at obtaining knowledge, generating beliefs,and promoting action is very wide. Both philosophy of science and science education havebeen concerned with critically assessing the virtues of the various scientific methods, especiallythe inductive and deductive ones. However, the emergence of new procedures specific to nonacademicsciences has encouraged the development of new reflective perspectives that can analyzethose virtues. From randomized controlled trials to epidemiological or clinical procedures, thePhilosophy of Science has been concerned with examining the virtues and also the defects of theirpractical set-up. The article assumes that modeling based on empirical evidence is a practice of high interest in linguistics. In order to substantiate this assumption, two philosophical approaches to scientific modeling distinguished by their respective research lines on the notion of representationare compared: the Representational and the Pragmatic. These accounts are then illustrated with abrief case taken from linguistics called language parsing, aimed at examining several particularsamples collected as evidence in early stages of experimental modeling. By way of conclusion, it isemphasized that both philosophical accounts provide analytical elements that are relevant for thekind of scientific reasoning around models and whose scope in science education may be of greatpractical interest.

Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram

Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports. A deep neural network (ECG-WMA-Net) was trained and outperformed both expert ECG interpretation and Q-wave indices, achieving an AUROC of 0.781 (CI: 0.762–0.799). The model was externally validated in a diverse cohort from Georgia (n = 2338), with an AUC of 0.723 (CI: 0.685–0.757). Explainability analysis revealed significant contributions from QRS and T-wave regions. This deep learning approach improves WMA screening accuracy, potentially addressing physiological differences not captured by standard ECG-based methods.

A comparison of reserved graph grammar and edge-based graph grammar

Abstract Context-sensitive graph grammars are natural formalisms for the generation and parsing of visual programming languages. Being intuitive, rigorous, and expressive tools, they have been frequently employed in a wide range of research fields. Nevertheless, the expressive powers of the existing context-sensitive formalisms have not been investigated yet. To this end, this paper is dedicated to the comparison of expressive powers of the two prevalent graph grammar formalisms, Reserved Graph Grammar (RGG) and Edge-based Graph Grammar (EGG). By introducing a special variant of RGG as an intermediary, the relation between them concerning the expressive power is revealed that the set of graph languages of EGG is a proper subset of that of RGG. Moreover, bidirectional transformations between EGG and the variant of RGG are accordingly established, allowing them to take the advantages of each other in applications.

BERT-based semantic analysis and fuzzy language parsing: interpreting the prophecies and historical event associations in the 40th Xiang of Tui Bei Tu

Based on the model of BERT, prophecies and homilies in the 40th Xiang of the Tui Bei Tu are analyzed semantically in this study for a possible connection in understanding historical events during the Qing Dynasty. It integrates natural language processing techniques into the research methodology, together with fuzzy parsing of language. Further, it gives ways of applying deep learning models for the recognition of metaphors and polysemous phrases in text. It also carries out time-series analysis based on a historical event dataset. Results from the study indicate that metaphors in prophetic phrases are closely related to historical events; just like the founding of the Qing Dynasty and the Ch'ing reform by Kangxi. Through sentiment analysis, it also presents the relation of sentiment fluctuations with major historical events. The conclusion drawn from the study mentions that the BERT model is very effective in parsing complex languages in ancient prophetic texts and provides a possibility of foretelling future social upheavals.

Cross-lingual dependency parsing for a language with a unique script

Abstract Syntactic parsing is one of the areas in Natural Language Processing. The development of large-scale multilingual language models has enabled cross-lingual parsing approaches, which allows us to develop parsers for languages that do not have treebanks available. However, these approaches rely on the assumption that languages share orthographic representations and lexical entries. In this article, we investigate methods for developing a dependency parser for Xibe, a low-resource language that is written in a unique script. We first investigate lexicalized monolingual dependency parsing experiments to examine the effectiveness of word, part-of-speech, and character embeddings as well as pre-trained language models. Results show that character embeddings can significantly improve performance, while pre-trained language models decrease performance since they do not recognize the Xibe script. We also train delexicalized monolingual models, which yield competitive results to the best lexicalized model. Since the monolingual models are trained on a very small training set, we also investigate lexicalized and delexicalized cross-lingual models. We use six closely related languages as source language, which cover a wide range of scripts. In this setting, the delexicalized models achieve higher performance than lexicalized models. A final experiment shows that we can increase performance of the cross-lingual model by combining source languages and selecting the most similar sentences to Xibe as training set. However, all cross-lingual parsing results are still considerably lower than the monolingual model. We attribute the low performance of cross-lingual methods to syntactic and annotation differences as well as to the impoverished input of Universal Dependency Part-of-Speech tags that the delexicalized model has access to.

Multi-Derivational Parsing of Vague Languages- The New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

An accurate transformer-based model for transition-based dependency parsing of free word order languages

Transformer models are the state-of-the-art in Natural Language Processing (NLP) and the core of the Large Language Models (LLMs). We propose a transformer-based model for transition-based dependency parsing of free word order languages. We have performed experiments on five treebanks from the Universal Dependencies (UD) dataset version 2.12. Our experiments show that a transformer model, trained with the dynamic word embeddings performs better than a multilayer perceptron trained on the state-of-the-art static word embeddings even if the dynamic word embeddings have a vocabulary size ten times smaller than the static word embeddings. The results show that the transformer trained on dynamic word embeddings achieves an unlabeled attachment score (UAS) of 84.17% for Urdu language which is ≈3.6% and ≈1.9% higher than the UAS scores of 80.56857% and 82.26859% achieved by the multilayer perceptron (MLP) using two static state-of-the-art word embeddings. The proposed approach is investigated for Arabic, Persian and Uyghur languages, in addition to Urdu, for UAS scores and the results suggest that the proposed solution outperform the MLP-based approaches.

Syntactic revision in wh-questions: developmental trajectory and the role of cognitive control

ABSTRACT In our study, we examine children’s and adults’ interpretation of argument wh-questions requiring syntactic revision using a questions-after-stories procedure, leading to three major findings. First, children aged 7–8 years, but not 5–6-year-olds, were found to preferably attach the fronted wh-element to the first available gap, rather than the second, indicating an adultlike incremental parsing strategy of active gap filling. Second, older children show a similar rate of revision as adults, higher than younger children, although a substantial rate of misinterpretations remains. Third, we find a significant link between the rate of revision and cognitive control skills measured with an N-back task. Implications for theories of language development and parsing are discussed.

Multi Task Learning Based Shallow Parsing for Indian Languages

Shallow Parsing is an important step for many Natural Language Processing tasks. Although shallow parsing has a rich history for resource rich languages, it is not the case for most Indian languages. Shallow Parsing consists of POS Tagging and Chunking. Our study focuses on developing shallow parsers for Indian languages. As part of shallow parsing we included morph analysis as well. For the study, we first consolidated available shallow parsing corpora for 7 Indian Languages (Hindi, Kannada, Bangla, Malayalam, Marathi, Urdu, Telugu) for which treebanks are publicly available. We then trained models to achieve state of the art performance for shallow parsing in these languages for multiple domains. Since analyzing the performance of model predictions at sentence level is more realistic, we report the performance of these shallow parsers not only at the token level, but also at the sentence level. We also present machine learning techniques for multitask shallow parsing. Our experiments show that fine-tuned contextual embedding with multi-task learning improves the performance of multiple as well as individual shallow parsing tasks across different domains. We show the transfer learning capability of these models by creating shallow parsers (only with POS and Chunk) for Gujarati, Odia, and Punjabi for which no treebanks are available. As a part of this work, we will be releasing the Indian Languages Shallow Linguistic (ILSL) benchmarks for 10 Indian languages including both the major language families Indo-Aryan and Dravidian as common building blocks that can be used to evaluate and understand various linguistic phenomena found in Indian languages and how well newer approaches can tackle them.

The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

Document Author Classification using Parsed Language Structure

Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statistical natural language parser were explored: all sub trees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.

Natural Language Generation and Parsing as Heuristic Planning Problems

This paper formulates the problems of natural language generation and parsing as particular instances of the classical planning problem. It assumes the existence of a Categorial Grammar lexicon from which the preconditions and effects of available actions are obtained. A declarative formalization of heuristics for action selection is used to guide the search for solutions. Heuristics for mapping formulas in the description logic DL-Lite (R,and) into English sentences and backwards, and examples of application to Human Robot Interaction (HRI) are presented to illustrate the effectiveness of the approach.

APPLICATION OF NATURAL LANGUAGE PARSING FOR IDENTIFYING NON-SURVEYED BOUNDARIES TOWARDS ENHANCED SYSTEMATIC LAND TITLING: RESULTS FROM PRELIMINARY EXPERIMENT

The need for the adoption of systematic land titling (SLT) in Nigeria cannot be overemphasised. Nonetheless, the problems of speed and cost of geospatial data acquisition, as well as identification of non-surveyed boundaries, remain unresolved, impeding the effectiveness of SLT for non-surveyed boundaries. The integration of language into Artificial Intelligence (AI) has allowed Natural Language Parsing (NLP) to effectively serve as a tool for communication between humans and computer systems. This study presents preliminary results of testing a prototype application that utilises NLP to convert textual descriptions into graphic sketches as a tool towards the production of a-priori sketches that can aid SLT in non-surveyed boundaries. The study determines that NLP alone cannot be used to achieve the required accuracy in geospatial data for SLT; however, the study concludes that NLP can be integrated alongside other ancillary information to enhance SLT in peri-urban regions.

Generation and Polynomial Parsing of Graph Languages with Non-Structural Reentrancies

Abstract Graph-based semantic representations are popular in natural language processing, where it is often convenient to model linguistic concepts as nodes and relations as edges between them. Several attempts have been made to find a generative device that is sufficiently powerful to describe languages of semantic graphs, while at the same allowing efficient parsing. We contribute to this line of work by introducing graph extension grammar, a variant of the contextual hyperedge replacement grammars proposed by Hoffmann et al. Contextual hyperedge replacement can generate graphs with non-structural reentrancies, a type of node-sharing that is very common in formalisms such as abstract meaning representation, but that context-free types of graph grammars cannot model. To provide our formalism with a way to place reentrancies in a linguistically meaningful way, we endow rules with logical formulas in counting monadic second-order logic. We then present a parsing algorithm and show as our main result that this algorithm runs in polynomial time on graph languages generated by a subclass of our grammars, the so-called local graph extension grammars.

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Nanjing Yunjin, one of China's traditional silk weaving techniques, is renowned for its unique local characteristics and exquisite craftsmanship, and was included in the Representative List of the Intangible Cultural Heritage of Humanity by UNESCO in 2009. However, with rapid development in weaving technology, ever-changing market demands, and shifting public aesthetics, Nanjing Yunjin, as an intangible cultural heritage, faces the challenge of survival and inheritance. Addressing this issue requires efficient storage, management, and utilization of Yunjin knowledge to enhance public understanding and recognition of Yunjin culture. In this study, we have constructed an intelligent question-answering system for Nanjing Yunjin digital resources based on knowledge graph, utilizing the Neo4j graph database for efficient organization, storage, and protection of Nanjing Yunjin knowledge, thereby revealing its profound cultural connotations. Furthermore, we adopted deep learning algorithms for natural language parsing. Specifically, we adopted BERT-based intent recognition technology to categorize user queries by intent, and we employed the BERT + BiGRU + CRF model for entity recognition. By comparing with BERT + BILSTM + CRF, BERT + CRF and BILSTM + CRF models, our model demonstrated superior performance in terms of precision, recall, and F1 score, substantiating the superiority and effectiveness of this model. Finally, based on the parsed results of the question, we constructed knowledge graph query statements, executed by the Cypher language, and the processed query results were fed back to the users in natural language. Through system implementation and testing, multiple indices including system response time, stability, load condition, accuracy, and scalability were evaluated. The experimental results indicated that the Nanjing Yunjin intelligent question-answering system, built on the knowledge graph, is able to efficiently and accurately generate answers to user’s natural language queries, greatly facilitating the retrieval and utilization of Yunjin knowledge. This not only reinforces the transmission, promotion, and application of Yunjin culture but also provides a paradigm for constructing other intangible cultural heritage question-answering systems based on knowledge graphs. This has substantial theoretical and practical significance for deeply exploring and uncovering the knowledge structure of human intangible heritage, promoting cultural inheritance and protection.

A hybrid part-of-speech tagger with annotated Kurdish corpus: advancements in POS tagging

Abstract With the rapid growth of online content written in the Kurdish language, there is an increasing need to make it machine-readable and processable. Part of speech (POS) tagging is a critical aspect of natural language processing (NLP), playing a significant role in applications such as speech recognition, natural language parsing, information retrieval, and multiword term extraction. This study details the creation of the DASTAN corpus, the first POS-annotated corpus for the Sorani Kurdish dialect. The corpus, containing 74,258 words and thirty-eight tags, employs a hybrid approach utilizing the bigram hidden Markov model in combination with the Kurdish rule-based approach to POS tagging. This approach addresses two key problems that arise with rule-based approaches, namely misclassified words and ambiguity-related unanalyzed words. The proposed approach’s accuracy was assessed by training and testing it on the DASTAN corpus, yielding a 96% accuracy rate. Overall, this study’s findings demonstrate the effectiveness of the proposed hybrid approach and its potential to enhance NLP applications for Sorani Kurdish.

TxLASM: A novel language agnostic summarization model for text documents

In Natural Language Processing (NLP) domain, the majority of automatic text summarization approaches depend on a prior knowledge of the language and/or the domain of the text being summarized. Such approaches requires language dependent part-of-speech taggers, parsers, databases, pre-structured lexicons, etc. In this research, we propose a novel automatic text summarization model, Text Documents - Language Agnostic Summarization Model (TxLASM), which is able to perform extractive text summarization task in language/domain agnostic manner. TxLASM depends on specific characteristics of the major elements of the text being summarized rather than its domain, context, or language and thus rules out the need for language dependent pre-processing tools, taggers, parsers, lexicons or databases. Within TxLASM, we present a novel technique for encoding the shapes of major text elements (paragraphs, sentences, n-grams and words); moreover, we present language independent preprocessing algorithms to normalize words and perform relative stemming or lemmatization. Those algorithms and its Shape-Coding technique enable the TxLASM to extract intrinsic features of text elements and score them statistically, and subsequently extract a representative summary that is independent of the text language, domain and context. TxLASM was applied on an English and Portuguese benchmark datasets, and the results were compared to twelve state-of-the-art approaches presented in recent literature. In addition, the model was applied on French and Spanish news datasets, and the results were compared to those obtained by standard commercial summarization tools. TxLASM has outperformed all the SOTA approaches as well as the commercial tools in all four languages while maintaining its language and domain agnostic nature.

DOMAS: Data Oriented Medical Visual Question Answering Using Swin Transformer

"The Medical Visual Question Answering problem is a joined Computer Vision and Natural Language Processing task that aims to obtain answers in natural language to a question, posed in natural language as well, regarding an image. Both the image and question are of a medical nature. In this paper we introduce DOMAS, a deep learning model that solves this task on the Med-VQA 2019 dataset. The method is based on dividing the task into smaller classification problems by using a BERT-based question classification and a unique approach that makes use of dataset information for selecting the suited model. For the image classification problems, transfer learning using a pre-trained Swin Transform based architecture is used. DOMAS uses a question classifier and seven image classifiers along with the image classifier selection strategy and achieves 0.616 strict accuracy and 0.654 BLUE score. The results are competitive with other state-of-the-art models, proving that our approach is effective in solving the presented task. 2010 Mathematics Subject Classification. 68T45, 68T50. 1998 CR Categories and Descriptors. I.2.7 [Artificial Intelligence]: Natural Language Processing – Language parsing and understanding; I.2.7 [Artificial Intelligence]: Applications and Expert Systems – Medicine and science. Key words and phrases. Medical Visual Question Answering, Swin Transformer."

A Hybrid Convolutional Bi-Directional Gated Recurrent Unit System for Spoken Languages of JK and Ladakhi

Spoken language identification is the process of recognising language in an audio segment and is the precursor for several technologies such as automatic call routing, language recognition, multilingual conversation, language parsing, and sentimental analysis. Language identification has become a challenging task for low-resource languages like Kashmiri and Ladakhi spoken in the UT’s of Jammu and Kashmir (JK) and Ladakh, India. This is mainly due to speaker variations like duration, moderator, and ambiance particularly when training and testing are done on different datasets whilst analysing the accuracy of language identification system in actual implementation, thus producing low accuracy results. In order to tackle this problem, we propose a hybrid convolutional bi-directional gated recurrent unit (Bi-GRU) utilising the effects of both static and dynamic behaviour of the audio signal in order to achieve better results as compared to state-of-the-art models. The audio signals are first converted into two-dimensional structures called Mel-spectrograms to represent the frequency distribution over time. To investigate the spectral behaviour of audio signals, we employ a convolutional neural network (CNN) that perceives Mel-spectrograms in multiple dimensions. The CNN-learned feature vector serves as input to the Bi-GRU that maintains the dynamic behaviour of the audio signal. Experiments are done on six spoken languages, i.e. Ladakhi, Kashmiri, Hindi, Urdu, English, and Dogri. The data corpora used for experimentation are the International Institute of Information Technology Hyderabad-Indian Language Speech Corpus (IIITH-ILSC) and the self-created data corpus for the Ladakhi language. The model is tested on two datasets, i.e. speaker-dependent and speaker-independent. Results show that when validating the efficiency of our proposed model on both speaker-dependent and speaker-independent datasets, we achieve optimal accuracies of 99% and 91%, respectively, thus achieving promising results in comparison to the state-of-the-art models available.

A Framework for Malay Computational Grammar Formalism based-on Enhanced Pola Grammar

In the era of IR4.0, Natural Language Processing (NLP) is one of the major focuses because text is stored digitally to code the information. Natural language understanding requires a computational grammar for syntax and semantics of the language in question for this information to be manipulated digitally. Many languages around the world have their own computational grammars for processing syntax and semantics. However, when it comes to the Malay language, the researchers have yet to come across a substantial computational grammar that can process Malay syntax and semantics based on a computational theoretical framework that can be applied in systems such as e-commerce. Hence, we intend to propose a formalism framework based on enhanced Pola Grammar with syntactic and semantic features. The objectives of this proposed framework are to create a linguistic computational formalism for the Malay language based on theoretical linguistic; implement templates for Malay words to handle syntax and semantic features in accordance with the enhanced Pola Grammar; and create a Malay Language Parser Algorithm that can be used for digital applications. To accomplish the objectives, the proposed framework will recursively formalise the computational Malay grammar and lexicon using a combination of solid theoretical linguistic foundations such as Dependency Grammar. A Malay parsing algorithm will be developed for the proposed model until the formalised grammar is deemed reliable. The findings of this indigenous Malay parser will help to advance Malay language applications in the digital economy.