Polysemy Research Articles

A Scoping Review of the Smart Irrigation Literature Using Scientometric Analysis

Background. Smart irrigation is a research field which grows very fast. It facilitates the contribution of technologies on smart agriculture. Smart irrigation is a broad topic with overwhelming literature published and available semantic ambiguity, so covering such a vast topic is not easy without scoping reviews. To enable researchers to gain a deep knowledge of structure of the field, a scientometric-based scoping review was conducted. Methods. The bibliometric data focused on smart irrigation from databases such as Scopus, Web of Science, and Google Scholar were downloaded, thoroughly merged, and cleaned to meet the inclusion criteria. These data were analyzed and clustered using K-means from VOSviewer. VOSviewer is used to create coauthor and coword occurrence network graphs from keywords, titles, and abstracts. Results. The findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of smart irrigation on agriculture. The leading institutions and researchers in the field and geographical distribution are from China, Israel, Australia, and Egypt. The leading main topics addressed in the field are IOT, smart irrigation, irrigation, water stress, energy, deep learning, soil moisture, and relations in the network. Conclusion. Smart irrigation (drip irrigation + IoT) in agriculture increases crop yield, increases water use efficiency, and decreases costs. In future work, large studies need to be conducted to establish and investigate the scope of smart irrigation research to reveal the knowledge structure, current state of practice, and key actors in the field.

Semantic-Context Graph Network for Point-Based 3D Object Detection

Point-based indoor 3D object detection has received increasing attention with the large demand for augmented reality, autonomous driving, and robot technology in the industry. However, the detection precision suffers from inputs with semantic ambiguity, i.e., shape symmetries, occlusion, and texture missing, which would lead that different objects appearing similar from different viewpoints and then confusing the detection model. Typical point-based detectors relieve this problem via learning proposal representations with both geometric and semantic information, while the entangled representation may cause a reduction in both semantic and spatial discrimination. In this paper, we focus on alleviating the confusion from entanglement and then enhancing the proposal representation by considering the proposal’s semantics and the context in one scene. A semantic-context graph network (SCGNet) is proposed, which mainly includes two modules: a category-aware proposal recoding module (CAPR) and a proposal context aggregation module (PCAg). To produce semantically clear features from entanglement representation, the CAPR module learns a high-level semantic embedding for each category to extract discriminative semantic clues. In view of further enhancing the proposal representation and leveraging the semantic clues, the PCAg module builds a graph to mine the most relevant context in the scene. With few bells and whistles, the SCGNet achieves SOTA performance and obtains consistent gains when applying to different backbones (0.9% ~ 2.4% on ScanNet V2 and 1.6% ~ 2.2% on SUN RGB-D for mAP@0.25). Code is available at https://github.com/dsw-jlu-rgzn/SCGNet.

러시아어 단어내적반의어(enantiosemy) 연구

This article is devoted to the problem of enantiosemy - one of the most special phenomena in the lexical-semantic aspect of the Russian language. This sphere attracts the interest of many researchers but not yet sufficiently studied and determined by nonsystematic approach of the described object. Research of an enantiosemy as multi-sided and difficult linguistic unit demands studying of a wide range of the questions explaining its contradictory nature. So, the author suggests to approach not only from classic term, so-called enantiosemy (ENS), which means “intra-word antonymy” but also consider the terms of antagonym or contronym, reflecting their asymmetric feature of semantic polarity. The article analyses the category of ENS and the mechanisms of their formation and classifies 3 main types of ENS. Firstly, ENS with superficial negation, secondly, ENS with inversion of diathesis (agent - patient), thirdly, interlingual ENS with panchronic adoption. ENS may be the result of a semantic reconsideration of the word and a revision of connotation in individual word usage such as metonymy and metaphor (one of the typical mechanisms of ENS). Also, linguistic and extralinguistic factors function as mechanism of ENS and reflect the points of view of the participants (agens-patiens) of the communicative situation. ENS is, first of all, multi-temporal in nature, and their opposite meanings, associated with the same root (Proto-Slavic). This semantic syncretism can be traced at different diachronical and synchronical stages of Slavic language development. That’s why ENS is widely represented in colloquial speech, dialects, jargon and conspicuous for two opposite meaning in many Slavic languages. The author comes to a conclusion that the essence of ENS with its semantic ambiguity lies not in the occasional deviation from the norm, but in a realization of effective communicative strategy.

A Big Data Grided Organization and Management Method for Cropland Quality Evaluation

A new gridded spatio-temporal big data fusion method is proposed for the organization and management of cropland big data, which could serve the analysis application of cropland quality evaluation and other analyses of geographic big data. Compared with traditional big data fusion methods, this method maps the spatio-temporal and attribute features of multi-source data to grid cells in order to achieve the structural unity and orderly organization of spatio-temporal big data with format differences, semantic ambiguities, and different coordinate projections. Firstly, this paper constructs a dissected cropland big data fusion model and completes the design of a conceptual model and logic model, constructs a cropland data organization model based on DGGS (discrete global grid system) and Hash coding, and realizes the unified management of vector data, raster data and text data by using multilevel grids. Secondly, this paper researches the evaluation methods of grid-scale adaptability, and generates distributed multilevel grid datasets to meet the needs of cropland area quality evaluation. Finally, typical data such as soil organic matter data, road network data, cropland area data, and statistic data in Da’an County, China, were selected to carry out the experiment. The experiment verifies that the method could not only realize the unified organization and efficient management of cultivated land big data with multimodal characteristics, but also support the evaluation of cropland quality.

Comparison of category and letter fluency tasks through automated analysis.

Category and letter fluency tasks are commonly used neuropsychological tasks to evaluate lexical retrieval. This study used validated automated methods, which allow for more expansive investigation, to analyze speech production of both category ("Animal") and letter ("F") fluency tasks produced by healthy participants (n = 36) on an online platform. Recordings were transcribed and analyzed through automated pipelines, which utilized natural language processing and automatic acoustic processing tools. Automated pipelines calculated overall performance scores, mean inter-word response time, and word start time; errors were excluded from analysis. Each word was rated for age of acquisition (AoA), ambiguity, concreteness, frequency, familiarity, word length, word duration, and phonetic and semantic distance from its previous word. Participants produced significantly more words on the category fluency task relative to the letter fluency task (p < 0.001), which is in line with previous studies. Wilcoxon tests also showed tasks differed on several mean speech measures of words, and category fluency was associated with lower mean AoA (p<0.001), lower frequency (p < 0.001), lower semantic ambiguity (p < 0.001), lower semantic distance (p < 0.001), lower mean inter-word RT (p = 0.03), higher concreteness (p < 0.001), and higher familiarity (p = 0.02), compared to letter fluency. ANOVAs significant interactions for fluency task on total score and lexical measures showed that lower category fluency scores were significantly related to lower AoA and higher prevalence, and this was not observed for letter fluency scores. Finally, word-characteristics changed over time and significant interactions were noted between the tasks, including word familiarity (p = 0.019), semantic ambiguity (p = 0.002), semantic distance (p=0.001), and word duration (p<0.001). These findings showed that certain lexical measures such as AoA, word familiarity, and semantic ambiguity were important for understanding how these tasks differ. Additionally, it found that acoustic measures such as inter-word RT and word duration are also imperative to analyze when comparing the two tasks. By implementing these automated techniques, which are reproducible and scalable, to analyze fluency tasks we were able to quickly detect these differences. In future clinical settings, we expect these methods to expand our knowledge on speech feature differences that impact not only total scores, but many other speech measures among clinical populations.

Artificial Intelligence Method for Accurate Translation of Fuzzy Semantics in English Language and Literature

In order to address the drawbacks of semantic ambiguity, inaccurate quantifiers, and low translation accuracy in traditional grammar-based translation methods, this paper proposes an artificial intelligence translation method based on semantic analysis for English fuzzy semantics. Firstly, a comprehensive analysis of English language semantics was carried out from different semantic levels such as language, knowledge, and pragmatics, and the key points of fuzzy semantics were identified. Then, key feature quantities for accurate translation of fuzzy semantics in English vocabulary and literature were constructed, and artificial intelligence methods were used to optimize fuzzy semantics. The experimental results show that the proposed method can avoid semantic understanding ambiguity and improve the accuracy of English language translation.

Implementing the map task in applied linguistics research: What, how, and why

The “map task” is an interactive, goal-driven, real-time conversational task used to elicit semi-controlled natural language production data. We present recommendations for creating a bespoke map task that can be tailored to individual research projects and administered online using a chat interface. As proof of concept, we present a case study exemplifying our own implementation, designed to elicit informal written communication in either English or Spanish. Eight experimental maps were created, manipulating linguistic factors including lexical frequency, cognate status, and semantic ambiguity. Participants (N = 40) completed the task in pairs and took turns (i) providing directions based on a pre-traced route, or (ii) following directions to draw the route on an empty map. Computational measures of image similarity (e.g., structural similarity index) between pre-traced and participant-traced routes showed that participants completed the task successfully; we describe use of this method for measuring task success quantitatively. We also provide a comparative analysis of the language elicited in English and Spanish. The most frequently used words were roughly equivalent in both languages, encompassing primarily commands and items on the maps. Similarly, abbreviations, swear words, and slang present in both datasets indicated that the task successfully elicited informal communication. Interestingly, Spanish turns were longer and displayed a wider range of morphologically complex forms. English, conversely, displayed strategies mostly absent in Spanish, such as the use of cardinal directions as a communicative strategy. We consider the online map task as a promising method for examining a variety of phenomena in applied linguistics research.

Multi-branch Context Awareness Network for Prostate MRI Segmentation

Prostate image segmentation is a precondition for the diagnosis of prostate diseases and subsequent treatment. However, the blurry organ edges and low image contrast make accurate segmentation difficult. In order to overcome the difficulties, this paper proposes an innovative Multi-branch Context Awareness Network (MBCA-Net) for prostate MRI segmentation. MBCA-Net uses the 3D UNet as the backbone network with an encoder-decoder framework. To improve the feature extraction capacity of the network, a multi-branch residual module with different convolution kernels is proposed. To better make use of extracted features and reduce semantic ambiguity, a context awareness module is used to fuse low-level features in the encoder and high-level features in the decoder in a full process manner. To improve the stability of the model during training, a mixed loss function is adopted. Experiments on the public dataset PROMISE12 show that our model’s Dice coefficient is 90.19%. Compared with other advanced techniques, the proposed model has better segmentation results.

Vector relation acquisition and scene knowledge for solving arithmetic word problems

This paper presents a vector relation-centric algorithm for solving arithmetic word problems (AWPs), which uses vector relation acquisition and scene knowledge to ensure the performances of problem understanding and symbolic solver correspondingly. The vector relation acquisition procedure builds on the synergy of the vector syntax-semantics method and the deep neural miner. Compared with the syntax-semantics method, the vector syntax-semantics method decreases not only the number of models but also semantic ambiguities and computational costs. For the scene knowledge, this paper proposes a scene-aware symbolic solver which infers relations obeying scene rules to decrease the occurrences of unwanted operations. Experimental results show that the proposed algorithm is superior to the high-performance baseline algorithm in both accuracy and computational cost. In accuracy, the proposed algorithm increased the accuracy by 3.9% on the sum of the three scene subsets due to the use of the scene knowledge and vector computing; as a result, it increased the accuracy by 0.5% on the sum of six authoritative datasets. In computational cost, the proposed algorithm decreased the computing cost by more than 50%. Thus, this paper makes a significant contribution to developing instruments for solving AWPs.

Adversarial Multi-task Learning for Efficient Chinese Named Entity Recognition

Named entity recognition (NER) is a fundamental task for information extraction applications. NER is challenging because of semantic ambiguities in academic literature, especially for non-Latin languages. Besides word semantic information, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this article, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. Specifically, we learn commonly shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging, and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks and combine the learned boundary information with the semantic information to improve entity recognition with multi-task learning. We then propose a compression method based on improved clustering to accelerate the proposed model. We conduct extensive experiments on four public benchmark datasets and two private datasets, compared with state-of-the-art baseline models, and the experimental results demonstrate that our model achieves considerable performance improvements on various evaluation datasets.

كلمة العدد (الإنجليزي)

كلمة العدد (الإنجليزي)

Spatial-Semantic Collaborative Graph Network for Textbook Question Answering

Textbook Question Answering (TQA) task requires answering questions by reasoning based on both the given diagrams and text context. There are mainly two challenges for the task. First, the diagrams are different from the natural images. Similar shapes or color blocks may express different semantics and there is also a large intra-topic variation for diagrams. Hence, the characteristics of visual semantic ambiguity and variable visual appearance make the diagram understanding more challenging. Second, for the text, the specific education domain with terminologies exists a great gap with the general domain. Therefore, it is difficult to represent the text semantics effectively using a text encoder pretrained in the general domain. In this paper, we propose a Spatial-Semantic Collaborative Graph Network (SSCGN) for TQA task, which can help enhance the diagram and text understanding and facilitate multimodal reasoning. Specifically, the Spatial-guided Semantic Enhancing (SSE) module fully exploits the spatial and semantic relationships between visual objects and OCR tokens to collaboratively enhance the diagram semantic understanding. Moreover, based on the semantically enhanced region representations of the SSE module, the Fine-grained Spatial-Aware Graph Network (FSA-GN) can help obtain richer relation-aware region representations for joint reasoning by capturing more fine-grained spatial relationships. We further propose multiple self-supervised auxiliary tasks to enhance the initial diagram and text semantic representations by pretraining the diagram encoder and text encoder. Extensive experiments and ablation studies are conducted to validate the effectiveness of SSCGN.

MLNet: a multi-level multimodal named entity recognition architecture.

In the field of human-computer interaction, accurate identification of talking objects can help robots to accomplish subsequent tasks such as decision-making or recommendation; therefore, object determination is of great interest as a pre-requisite task. Whether it is named entity recognition (NER) in natural language processing (NLP) work or object detection (OD) task in the computer vision (CV) field, the essence is to achieve object recognition. Currently, multimodal approaches are widely used in basic image recognition and natural language processing tasks. This multimodal architecture can perform entity recognition tasks more accurately, but when faced with short texts and images containing more noise, we find that there is still room for optimization in the image-text-based multimodal named entity recognition (MNER) architecture. In this study, we propose a new multi-level multimodal named entity recognition architecture, which is a network capable of extracting useful visual information for boosting semantic understanding and subsequently improving entity identification efficacy. Specifically, we first performed image and text encoding separately and then built a symmetric neural network architecture based on Transformer for multimodal feature fusion. We utilized a gating mechanism to filter visual information that is significantly related to the textual content, in order to enhance text understanding and achieve semantic disambiguation. Furthermore, we incorporated character-level vector encoding to reduce text noise. Finally, we employed Conditional Random Fields for label classification task. Experiments on the Twitter dataset show that our model works to increase the accuracy of the MNER task.

Arabic Ontology: Linguistic Engineering Foundations

This scientific intervention aims to raise the issue of the automatic processing of natural languages. Especially at its levels related to ontological studies, enabling the machine to recognize information and invest it accurately, beyond the constraints of semantic ambiguity is considered a scientific challenge. This would not be possible without relying on the contextual detection process of linguistic engineering, which is based on a hybrid approach that combines both statistical and linguistic methods. It is an approach that falls within the context of platform linguistics, or what is termed "fourth-generation linguistics", a natural outgrowth of the digital revolution, based on its horizontal extension in various domains and fields of knowledge, thus establishing a new indicative model in which platforms with linguistic and computer interact. In this context, the associative aspects within the compositional linguistic perceptions are a focal point in operating research operations that fall within the automatic processing of natural language, given the nature of its theoretical and methodological architecture with an empirical inductive basis. It also enables the building of computer platforms by preparing morphological, synthetic, semantic, and pragmatic analyzers. Investing in the advanced technological tools provided by the artificial intelligence system; especially in its aspects related to machine learning, deep learning, and neural network; will enable the provision of a linguistic platform capable of developing paths of teaching the Arabic language to non-native speakers.

Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method.

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.

A Semantic Representation Method of Building Codes Applied to Compliance Checking

Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.

THE TERM COMBINATION AND THE METAPHOR IN THE OFFICIAL BUSINESS DOCUMENT: COGNITIVE ASPECT

THE TERM COMBINATION AND THE METAPHOR IN THE OFFICIAL BUSINESS DOCUMENT: COGNITIVE ASPECT

BIOREALM—An ontology of comparative biogeography: New insights into the semantics of biodiversity conservation

AbstractAimWe aimed to apply ontological techniques to address semantic ambiguities in protected area and conservation informatics. By doing so, we aimed to create a coherent, machine‐actionable semantic representation of the biogeographic areas (which often overlap protected areas) to support more efficient and standardized informatics, supporting research and decision‐making. We present BIOREALM, the first informatic ontology for comparative biogeography.LocationGlobal.TaxonAny taxon can be integrated in BIOREALM.MethodsWe convert a cladogram of biogeographic areas—generated by a process known as bioregionalization—into a series of ontological classes. Areas of endemism are treated as formal objects related by hierarchical relationships and constrained by a condition of monophyly. We use semantic web approaches to extend the Environment Ontology (ENVO) with classes for (often semantically confounded) biogeographic entities, including biogeographic areas, areas of endemism and endemic areas. We applied this approach to a bioregionalization of Australia as a case study. In all, 20 subregions which are part of the Austral Bioregionalisation Atlas have been selected for the study and integrated in BIOREALM.ResultsWe have created an ontology—formatted in the Web Ontology Language and adhering to the practices of the Open Biomedical and Biological Ontology Foundry—which provides a rigorous, extensible and machine‐actionable framework that can improve biogeographic analyses and interoperability between systems. One main class and 20 individuals per class were implemented.Main ConclusionsBIOREALM encodes a model‐theoretic view of endemism using semantic web approaches, offering new avenues to express and analyse biogeographic units. This approach offers a means to identify monophyletic biogeographic areas for conservation, based on specific combinations of monophyletic endemic taxa. Such an ontology provides knowledge representation solutions which supports interoperability along the FAIR (Findable, Accessible, Interoperable, Reusable) principles, thus fostering more consistent ecological informatics.

Joint Intent Detection Model for Task-oriented Human-Computer Dialogue System using Asynchronous Training

How to accurately understand low-resource languages is the core of the task-oriented human-computer dialogue system. Language understanding consists of two sub-tasks, i.e., intent detection and slot filling. Intent detection still faces challenges due to semantic ambiguity and implicit intentions with users’ input. Moreover, separately modeling intent detection and slot filling significantly decrease the correctness and relevance between questions and answers. To address these issues, we propose a joint intent detection method using asynchronous training strategy. The proposed method firstly encodes local text information extracted by CNN and relationship information among words emphasized by attention structure. Later, a joint intent detection model with asynchronous training strategy is proposed by either fusing hidden states of intent detection and slot filling layers, or adopting the key information to fine-tune the whole network, greatly increasing the relevance of intent detection and slot filling subtasks. The accuracy achieved by the proposed method tested on an open-source airline travel dataset and a self-collected electricity service dataset, i.e., ATIS and ECSF, are 97.49% and 89.68%, respectively, which proves the effectiveness of joint learning and asynchronous training.

Semantic-guided 3D building reconstruction from triangle meshes

Planar primitives tend to be incorrectly detected or incomplete in complex scenes where adhesions exist between different objects, resulting in topology errors in the reconstructed models. We propose a semantic-guided building reconstruction method known as semantic-guided reconstruction (SGR), which is capable of achieving the independence and integrity of building models in two key stages. In the first stage, the space partition is represented by a 2.5D convex cell complex and is capable of restoring planar primitives that are easily lost and can further infer the potential structural adaptivity. The second stage incorporates semantic information into a graph-cut formulation that can assist in the independent reconstruction of buildings while eliminating interference from the surrounding environment. Our experimental results confirmed that the SGR method can authentically reconstruct weakly observed surfaces. Furthermore, qualitative and quantitative evaluations show that SGR is suitable for reconstructing surfaces from insufficient data with semantic and geometric ambiguity or semantic errors and can obtain watertight models considering fidelity, integrity and time complexity.