Parsing Algorithm Research Articles

Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry

Abstract Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.

On the power of local graph expansion grammars with and without additional restrictions

We study graph expansion grammars, a type of graph grammar that has recently been introduced with motivations in natural language processing. Graph expansion generalizes the well-known hyperedge replacement. In contrast to the latter, the former is able to generate graph languages of unbounded treewidth, like the set of all graphs. In an earlier paper, the complexity of the membership problem of the generated languages was studied, the main result being a polynomial parsing algorithm for local DAG expansion grammars (there called local DAG expansion grammars), a subclass of graph expansion grammars that generates directed acyclic graphs. Here, we study the generative power of local graph expansion grammars. While they, unrestricted, are able to simulate Turing machines, we identify natural restrictions that give rise to a pumping lemma and ensure that the generated languages have regular path languages as well as a semi-linear Parikh image.

From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions.

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Considering the prevailing rule of Large Language Models (LLMs) applications and the benefits of XML in a compiler context. This manuscript explores the synergistic integration of Large Language Models with XML-based compiler tools and advanced computing technologies. Marking a significant stride toward redefining compiler construction and data representation paradigms. As computing power and internet proliferation advance, XML emerges as a pivotal technology for representing, exchanging, and transforming documents and data. This study builds on the foundational work of Chomsky's Context-Free Grammar (CFG). Recognized for their critical role in compiler construction, to address and mitigate the speed penalties associated with traditional compiler systems and parser generators through the development of an efficient XML parser generator employing compiler techniques. Our research employs a methodical approach to harness the sophisticated capabilities of LLMs, alongside XML technologies. The key is to automate grammar optimization, facilitate natural language processing capabilities, and pioneer advanced parsing algorithms. To demonstrate their effectiveness, we thoroughly run experiments and compare them to other techniques. This way, we call attention to the efficiency, adaptability, and user-friendliness of the XML-based compiler tools with the help of these integrations. And the target will be the elimination of left-recursive grammars and the development of a global schema for LL(1) grammars, the latter taking advantage of the XML technology, to support the LL(1) grammars construction. The findings in this research not only underscore the signification of these innovations in the field of compilation construction but also indicate a paradigm move towards the use of AI technologies and XML in the context of the resolution of programming traditional issues. The outlined methodology serves as a roadmap for future research and development in compiler technology, which paves the way for open-source software to sweep across all fields. Gradually ushering in a new era of compiler technology featuring better efficiency, adaptability, and all CFGs processed through existing XML utilities on a global basis.

A patient safety knowledge graph supporting vaccine product development

BackgroundKnowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources.MethodsWe designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs).ResultsWe import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster.ConclusionsOrganizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., “Produce vaccine-X safety profile for adverse event-Y by country instead of age-range”).

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding.

Automatic parsing of human anatomies at the instance-level from 3D computed tomography (CT) is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) can all make anatomy parsing algorithms vulnerable. In this work, we explore how to leverage and implement the successful detection-then-segmentation paradigm for 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering the complicated shapes, sizes, and orientations of anatomies, without loss of generality, we present a nine degrees of freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost inference efficiency. We have validated our method on three medical imaging parsing tasks: ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the high efficiency and effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%), compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine parsing and abdominal multi-organ segmentation, our method achieves competitive results on par with stateof-the-art methods on the public CTSpine1K dataset and FLARE22 competition, respectively. Our annotations, code, and models are available at: Med-Query.

Information Technology Intelligent Search of Content in E-commerce Systems

The article describes the process of developing intelligent search technology for content for the implementation of the module of e-commerce systems for forming a list of recommendations for regular users. Intelligent search of content is based on methods of linguistic analysis, modern algorithms for parsing and finding words, and recommendations based on user preferences. The main components of such a search are the parsing of text strings, the selection of keywords, the spelling check, the recognition of common abbreviations and acronyms, the semantic analysis of the text, the search by relevance with the extraction of synonyms, filters and sorting. A web application based on Java and Elasticsearch was developed with the implementation of a recommender system based on a collaborative filtering algorithm. The purpose of the work is to develop the technology of intelligent product search with the formation of a list of recommendations for the user. The object of the research is the processes of intelligent search with the possibility of generating recommendations for users in the field of any e-commerce without reference to the categorization of goods/services, etc. The subject of research is the methods and means of intelligent search of recommender systems based on the Collaborative Filtering algorithm for the formation of product recommendations for users, which is oriented on general coincidences of the choices of similar users. During the experimental testing of the developed system, a number of search queries were conducted with and without the NLP algorithm, the results of which demonstrated an improvement in system performance within the range of 15–95 % depending on the keyword and the presence/absence of errors in the search words. A comparison of the speed of execution of requests with already existing systems was also carried out. Yes, the amount of data in the storage may differ (error when comparing 60–70 ms). For example, a query that consists of 1 or 2 words will be found much faster by 20–70 ms compared to its counterparts. But for 3 and more, results are about the same – 9–20 ms faster.

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.

On Total-Order HTN Plan Verification with Method Preconditions – An Extension of the CYK Parsing Algorithm

On Total-Order HTN Plan Verification with Method Preconditions – An Extension of the CYK Parsing Algorithm

SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from the research community. However, the ERC task is challenging, largely due to the complex and unstructured properties of multi-party conversations. Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. We first introduce a dialogue parsing algorithm into ERC and further improve the algorithm through a transfer learning method. Moreover, we leverage different symbolic knowledge graph relations to learn knowledge-enhanced features for the ERC task. Extensive experiments on three benchmarks demonstrate that both dialogue structure graphs and symbolic knowledge are beneficial to the model performance on the task. Additionally, experimental results indicate that the proposed model surpasses baseline models on several indices.

Tunnel Parsing with Ambiguous Grammars

Abstract The article proposes an addition to the tunnel parsing algorithm that enables it to parse grammars having countable repetitions and configurations of grammar elements generating empty words without refactoring the grammar. The equivalency of trees built by the use of ambiguous grammar is discussed. The class of the ε-ambiguous grammars is defined as a subclass of the ambiguous grammars relative to these trees. The ε-deterministic grammars are then defined as a subclass of the ε-ambiguous grammars. A technique for linearly parsing on the basis of non-left recursive ε-deterministic grammars with the tunnel parsing algorithm is shown.

The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing.

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal-those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008a) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.

A Derivative-based Parser Generator for Visibly Pushdown Grammars

In this article, we present a derivative-based, functional recognizer and parser generator for visibly pushdown grammars. The generated parser accepts ambiguous grammars and produces a parse forest containing all valid parse trees for an input string in linear time. Each parse tree in the forest can then be extracted also in linear time. Besides the parser generator, to allow more flexible forms of the visibly pushdown grammars, we also present a translator that converts a tagged CFG to a visibly pushdown grammar in a sound way, and the parse trees of the tagged CFG are further produced by running the semantic actions embedded in the parse trees of the translated visibly pushdown grammar. The performance of the parser is compared with popular parsing tools, including ANTLR, GNU Bison, and other popular hand-crafted parsers. The correctness and the time complexity of the core parsing algorithm are formally verified in the proof assistant Coq.

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.

Exact Recursive Probabilistic Programming

Recursive calls over recursive data are useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm.

On Parsing Programming Languages with Turing-Complete Parser

A new parsing method based on the semi-Thue system is described. Similar to, but with more efficient implementation than Markov normal algorithms, it can be used for parsing any recursively enumerable language. Despite its computational power, it is meant to be used primarily for parsing programming and domain-specific languages. It enables a straightforward simulation of a number of existing parsing algorithms based on context-free grammars. The list includes both top-down shift-produce methods (such as SLL and LL) and bottom-up shift-reduce methods (such as LALR and LR), as well as mixed top-down-and-bottom-up methods such as LLLR. To justify the use of the new parsing method, the paper provides numerous examples of how a parser can actually be made in practice. It is advised that the main part of the parser is based on some simple well-established approach, e.g., SLL(1), while syntactically more complicated phrases can be parsed by exploiting the full power of the new parser. These phrases may either be extensions to the original language or some embedded domain-specific language. In all such and similar cases, no part of the language is restricted to be context-free. In fact, context-sensitive languages can be handled quite efficiently.

CCFNet: Cross-Complementary fusion network for RGB-D scene parsing of clothing images

Schemes to complement context relationships by cross-scale feature fusion have appeared in many RGB-D scene parsing algorithms; however, most of these works conduct multi-scale information interaction after multi-modal feature fusion, which ignores the information loss of the two modes in the original coding. Therefore, a cross-complementary fusion network (CCFNet) is designed in this paper to calibrate the multi-modal information before feature fusion, so as to improve the feature quality of each mode and the information complementarity ability of RGB and the depth map. First, we divided the features into low, middle, and high levels, among which the low-level features contain the global details of the image and the main learning features include texture, edge, and other features. The middle layer features contain not only some global detail features but also some local semantic features. Additionally, the high-level features contain rich local semantic features. Then, the feature information lost in the coding process of low and middle level features is supplemented and extracted through the designed cross feature enhancement module, and the high-level features are extracted through the feature enhancement module. In addition, the cross-modal fusion module is designed to integrate multi-modal features of different levels. The experimental results verify that the proposed CCFNet achieves excellent performance on the RGB-D scene parsing dataset containing clothing images, and the generalization ability of the model is verified by the dataset NYU Depth V2.

A Bibliometric Perspective on AI Research for Job-Résumé Matching.

The search for the right person for the right job, or in other words the selection of the candidate who best reflects the skills demanded by employers to perform a specific set of duties in a job appointment, is a key premise of the personnel selection pipeline of recruitment departments. This task is usually performed by human experts who examine the résumé or curriculum vitae of candidates in search of the right skills necessary to fit the vacant position. Recent advances in AI, specifically in the fields of text analytics and natural language processing, have sparked the interest of research on the application of these technologies to help recruiters accomplish this task or part of it automatically, applying algorithms for information extraction, parsing, representation, and matching of résumés and job descriptions, or sections within. In this study, we aim to better understand how the research landscape in this field has evolved. To do this, we follow a multifaceted bibliometric approach aimed at identifying trends, dynamics, structures, and visual mapping of the most relevant topics, highly cited or influential papers, authors, and universities working on these topics, based on a publication record retrieved from Scopus and Google Scholar bibliographic databases. We conclude that, unlike a traditional literature review, the bibliometric-guided approach allowed us to discover a more comprehensive picture of the evolution of research in this subject and to clearly identify paradigm shifts from the earliest stages to the most recent efforts proposed to address this problem.

Tunnel Parsing with the Token’s Lexeme

Abstract The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.