Concept Vector Research Articles

Stylistic Conversion and Linguistic Beauty Reproduction in English Translation-Based on Neurosemantic Analysis

Abstract The core of translation is language conversion, but it should be more of an aesthetic experience, a transformation of different ways of thinking. This paper proposes a neural semantic analysis method for English translation based on text similarity calculation. The technique uses a large-scale corpus to train the vector space model of words, establishes a concept-context matrix through the term-document matrix, and determines each value of the concept vector with a weight according to the literary translation in the knowledge base. The information extraction and evaluation of the English-translated text through semantic similarity calculation provides a theoretical basis for the subsequent analysis of style conversion and linguistic, and aesthetic reproduction of the English-translated text. The study will take the Lin Yutang translation and the Ricardian translation of The Analects of Confucius as an example to develop the analysis of style transformation and reproduction of linguistic beauty of the English-translated text from both lexical and syntactic levels. The results show that the lexical density of Lin Yutang’s translation is 0.5866, while that of Rie Jacob’s translation is 0.5334, with the former significantly larger than the latter. It shows that Lin Yutang’s translation of The Analects of Confucius adopts a naturalization strategy, i.e., To disseminate Chinese culture in a way that is authentic, familiar to English readers, and easy to understand, so as to make his linguistic expression more emotional.

Timeline Binder: A Caption-based Approach for Creating Semantic Video Timeline

Video is an important type of multimedia which has myriad characteristics such as richer content and little prior structure. Major applications such as surveillance, medicine, education, entertainment, and sports heavily use videos. Internet protocol video is dominating the World’s Internet traffic. Large number of videos are made and uploaded daily. Looking for topics of one’s interest in these videos is a cumbersome task. Mostly, people are only interested in small portions of videos as opposed to the whole video. It is better to look for small but related segments of interest. Our proposed system Timeline Binder is an attempt to tackle this problem. For Timeline Binder, we have created our own dataset of online videos with subtitles. Based on those subtitles’ files, a concept vector is obtained. If a concept vector has a match for a user’s query tag, then timed segments for all related videos to that matched concept in the concept vector are combined into a single timeline thus making all related segments into a single video. In this research, we present performance measurements between Timeline Binder and other available applications such as Google2SRT and NR8. The results show that Timeline Binder performs better than Google2SRT and NR8.

Семантический аспект деконструкции морали (на материале немецкого языка)

The study aims to determine the semantic features of the transforming morality and its discourse. The scientific novelty of the work lies in the author’s approach to identifying and describing the semantics of morality and moral discourse, taking into account the most typical collocations of the keyword ‘Moral’ acting as a subject. As a result of the study, it was found that metaphors, metaphtonymies and intertextuality serve as the semantic markers of the transforming morality as a subject in the type of discourse under consideration. The analysis of examples of moral discourse, in which ‘Moral’ acts as a subject, has shown that intertextuality is manifested in the use of precedent phenomena in moral discourse referring to moral issues. Aphorisms can undergo formal transformations and receive occasional components without changing the negative axiological vector of moral discourse. It is determined that the sports metaphor ‘auf der Strecke bleiben’, which is based on the metaphorical model LIFE is A SPORTS COMPETITION, is actualised in moral discourse. The conceptual vector of rivalry reflects the real worldview and the place that morality occupies in it: morality appears as a loser, a player who has lost the race, who has conceded victory to politics, art, profit and economic progress. The metaphtonymies ‘Moral verlangt’, ‘Moral herrscht’ have negative evaluativeness. Metaphors and metaphtonymies in moral discourse emphasise the secondary nature of morality at the present stage of societal development.

Concept2Vec: concept vector generation for biomedical literature using concept modelling

<span>In the biomedical field due to tremendous medical research, every year the medical papers generated is very huge. In order to process these tremendous amounts of textual data, the information retrieval techniques are combined with machine learning methods like natural language processing (NLP) for finding the useful insights in data for NLP tasks. In this work, we have addressed the challenges involved in existing NLP techniques like Word2Vec, Doc2Vec, and Biosent2Vec and followed by the traditional classification of articles with Concept2Vec and random forest classification. Concept vector techniques are emerging methods in NLP and in information retrieval systems. Pretrained concept encoders are available in Machine learning, whereas no such exists in biomedical domain. In this paper a proposal on Concept2Vec trained from biomedical text is highlighted. Our work accurately emphasizes on the quality of semantics defined between the medical terms, which can help the medical practitioners in taking errorless decisions. In this work 20 thousand biomedical documents are considered for Concept2Vec embeddings and the proposed approach have shown best concept similarity in terms of relevant measures compared with existing approaches such as Word2Vec, global vectors (GloVe), fastText, and BioSentVec, bidirectional encoder representations from transformers (BERT), biomedical-BERT.</span>

Culture as a Conceptual Vector of Economic Modernization

The article shows that at present the processes of total marketization have spread to the sphere of artistic culture, in which more and more the value of a creator and the results of his activity is evaluated in terms of market and capital. It becomes one of the factors of the economy stagnation that lasts for decades, in particular, in Russia. Meanwhile, the existing experience of modernization in the USSR shows that there is a powerful feedback when culture and, in particular, art, become an important factor and conceptual vector of socio-economic and technological modernization. The means for this can be and have been historically long-term strategic development programs linking together technological, scientific, economic, educational and cultural transformations, mass labor enthusiasm, the inclusion of workers in social creativity and other relations and institutions that transform a person from a passive object (an employee, consumer) into an active creator. The history of the USSR gives contradictory examples of such relations formation (the GOELRO plan, etc.).

Characterization of tissue types in basal cell carcinoma images via generative modeling and concept vectors.

The promise of machine learning methods to act as decision support systems for pathologists continues to grow. However, central to their successful adoption must be interpretable implementations so that people can trust and learn from them effectively. Generative modeling, most notable in the form of adversarial generative models, is a naturally interpretable technique because the quality of the model is explicit from the quality of images it generates. Such a model can be further assessed by exploring its latent space, using human-meaningful concepts by defining concept vectors. Motivated by these ideas, we apply for the first time generative methods to histological images of basal cell carcinoma (BCC). By simultaneously learning to generate and encode realistic image patches, we extract feature rich latent vectors that correspond to various tissue morphologies, namely BCC, epidermis, keratin, papillary dermis and inflammation. We show that a logistic regression model trained on these latent vectors can achieve high classification accuracies across 6 binary tasks (86-98%). Further, by projecting the latent vectors onto learned concept vectors we can generate a score for the absence or degree of presence for a given concept, providing semantically accurate "conceptual summaries" of the various tissues types within a patch. This can be extended to generate multi-dimensional heat maps for whole-image specimens, which characterizes the tissue in a similar way to a pathologist. We additionally find that accurate concept vectors can be defined using a small labeled dataset.

Efficient bilingual lexicon extraction from comparable corpora based on formal concepts analysis

AbstractBilingual corpora are an essential resource used to cross the language barrier in multilingual natural language processing tasks. Among bilingual corpora, comparable corpora have been the subject of many studies as they are both frequent and easily available. In this paper, we propose to make use of formal concept analysis to first construct concept vectors which can be used to enhance comparable corpora through clustering techniques. We then show how one can extract bilingual lexicons of improved quality from these enhanced corpora. We finally show that the bilingual lexicons obtained can complement existing bilingual dictionaries and improve cross-language information retrieval systems.

Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study

BackgroundThe secondary use of structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high dimensionality of the data representation. Constructing an effective representation for sEMR data is becoming more and more crucial for subsequent data applications.ObjectiveWe aimed to apply the embedding technique used in the natural language processing domain for the sEMR data representation and to explore the feasibility and superiority of the embedding-based feature and patient representations in clinical application.MethodsThe entire training corpus consisted of records of 104,752 hospitalized patients with 13,757 medical concepts of disease diagnoses, physical examinations and procedures, laboratory tests, medications, etc. Each medical concept was embedded into a 200-dimensional real number vector using the Skip-gram algorithm with some adaptive changes from shuffling the medical concepts in a record 20 times. The average of vectors for all medical concepts in a patient record represented the patient. For embedding-based feature representation evaluation, we used the cosine similarities among the medical concept vectors to capture the latent clinical associations among the medical concepts. We further conducted a clustering analysis on stroke patients to evaluate and compare the embedding-based patient representations. The Hopkins statistic, Silhouette index (SI), and Davies-Bouldin index were used for the unsupervised evaluation, and the precision, recall, and F1 score were used for the supervised evaluation.ResultsThe dimension of patient representation was reduced from 13,757 to 200 using the embedding-based representation. The average cosine similarity of the selected disease (subarachnoid hemorrhage) and its 15 clinically relevant medical concepts was 0.973. Stroke patients were clustered into two clusters with the highest SI (0.852). Clustering analyses conducted on patients with the embedding representations showed higher applicability (Hopkins statistic 0.931), higher aggregation (SI 0.862), and lower dispersion (Davies-Bouldin index 0.551) than those conducted on patients with reference representation methods. The clustering solutions for patients with the embedding-based representation achieved the highest F1 scores of 0.944 and 0.717 for two clusters.ConclusionsThe feature-level embedding-based representations can reflect the potential clinical associations among medical concepts effectively. The patient-level embedding-based representation is easy to use as continuous input to standard machine learning algorithms and can bring performance improvements. It is expected that the embedding-based representation will be helpful in a wide range of secondary uses of sEMR data.

A very Nordic set of concerns?

This article identifies visionary circumspection as a conceptual vector running through Nordic urban research – a diverse enterprise with robust empirical outputs, but relatively little premium placed on the generation of urban theory. To foster new cross-regional conversations that can bolster the theoretical fecundity of Nordic urban studies, the author overviews key themes in the region’s urban research portfolio – well-being, diversification and socio-spatial transformations, governance and development models, and sustainable futures – and then delineates sites in other world regions that are grappling with related topics but sometimes with different approaches or conceptualizations. By situating Nordic urban research vis-a-vis these disparate sites and theoretical repertoires, the article aims to leverage Nordic self-regard and open up discussions that could enable more ambitious theoretical engagements between the region’s cities and the rest of the world.

Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System.

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed methodology. Different statistical tests have also been made to observe that the method is statistically significant.

Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

Mobile Cloud Computing (MCC) is a computing model that makes mobile devices resourceful by executing mobile applications (apps) in the cloud and storing data in cloud servers. MCC faces several security threats in both the Cloud and Mobile environments. Among several threats, malicious apps are the most threatening ones, because they can perform various malicious activities in both environments. The traditional malware detection methods may not detect new types of malware or rapidly changing malware behavior. So, there is a need to develop an accurate model for detecting malicious apps in the MCC environment. Scalability and Knowledge Reusability are challenging issues in existing detection methods. To overcome these issues, the proposed model uses an effective Ontology-based intelligent model based on app permissions to detect malware apps. This model extracts the relationship between the static features from the apps and builds an Apps Feature Ontology (AFO). A concept vector set for apps is created using the items obtained from the AFO. The most discriminant features are selected using optimization algorithms like Particle Swarm Optimization, Social Spider Algorithm (SSA), and Gravitational Search Algorithm to reduce the dimension of the concept vector set. Various classifiers are applied to the reduced set. The efficiency of the proposed approach was evaluated on datasets obtained from the AndroZoo repository and VirusShare. The experimental results reveal that the proposed model can correctly detect malware using the Random Forest (RF) classifier with SSA and achieve higher detection accuracy with the lesser fall-out and less detection speed than existing Android malware detection techniques. Specifically, RF with SSA obtained higher accuracy, F1-score, and reduction in the fall-out of 94.11%, 93%, and 3%, respectively.

Application of convolution neural network in web query session mining for personalised web search

In this paper, a deep learning convolution neural network (CNN) is applied in web query session mining for effective personalised web search. The CNN extracts high-level continuous clicked document/query concept vector for semantic clustering of documents. The CNN model is trained to generate document/query concept vector based on clickthrough web query session data. Training of CNN is done using backpropagation based on stochastic gradient descent maximising the likelihood of relevant document given a user search query. During web search, search query concept vector is generated and compared with semantic clusters means to select the most similar cluster for web document recommendations. The experimental results were analysed based on average precision of search results and loss function computed during training of CNN. The improvement in precision of search results as well as decrease in loss value proves CNN to be effective in capturing semantics of web user query sessions for effective information retrieval.

Statistics Analysis of Multi-Modality and Text-Derived Representations on Concept Learning

When learning concepts, cognitive psychology research has revealed that there are two types of concept representation in the human brain: language-derived codes and sensory-derived codes. Thus, in terms of computing, do the multimodal concept vectors and linguistic concept vectors have differences and similarities as human beings? Four experiments are presented in this work, all focused on multi-modality representations labeled by psychologists and text-derived representations generated by computer scientists for concept learning, and the results demonstrate that 1)for the same concept, both forms of representations can properly reflect the concept, but 2) the representational similarity analysis findings reveal that the two types of representations are significantly different, 3) as the concreteness of a concept grows larger, the multi-modality representation of the concept becomes closer to human beings than the text-derived representation, and 4) we verified that combining the two improves the concept representation.

Application of Fuzzy C-Means Clustering and Semantic Ontology in Web Query Session Mining for Intelligent Information Retrieval

Information retrieval based on keywords search retrieves irrelevant documents because of vocabulary gap between document content and search queries. The keyword vector representation of web documents is very high dimensional, and keyword terms are unable to capture the semantic of document content. Ontology has been built in various domains for representing the semantics of documents based on concepts relevant to document subject. The web documents often contain multiple topics; therefore, fuzzy c-means document clustering has been used for discovering clusters with overlapping boundaries. In this paper, the method is proposed for intelligent information retrieval using hybrid of fuzzy c-means clustering and ontology in query session mining. Thus, use of fuzzy clusters of web query session concept vector improve quality of clusters for effective web search. The proposed method was evaluated experimentally, and results show the improvement in precision of search results.

A Wikipedia-based semantic tensor space model for text analytics

This paper proposes a third-order tensor space model that represents textual documents, which contains the 'concept' space independently of the 'document' and 'term' spaces. In the vector space model (VSM), a document is represented as a vector in which each dimension corresponds to a term. In contrast, the model described here represents a document as a matrix. Most current text mining algorithms only take vectors as their input, but they suffer from 'term independence' and 'loss of term senses' issues. To overcome these problems, we incorporate the 'concept' as a distinct space in the VSM. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia Encyclopedia, which has been evaluated as world knowledge and used to improve many text-mining algorithms. Through experiments using two popular document corpora, we demonstrate the superiority of the model in terms of text clustering and text classification.

A Wikipedia-based semantic tensor space model for text analytics

This paper proposes a third-order tensor space model that represents textual documents, which contains the 'concept' space independently of the 'document' and 'term' spaces. In the vector space model (VSM), a document is represented as a vector in which each dimension corresponds to a term. In contrast, the model described here represents a document as a matrix. Most current text mining algorithms only take vectors as their input, but they suffer from 'term independence' and 'loss of term senses' issues. To overcome these problems, we incorporate the 'concept' as a distinct space in the VSM. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia Encyclopedia, which has been evaluated as world knowledge and used to improve many text-mining algorithms. Through experiments using two popular document corpora, we demonstrate the superiority of the model in terms of text clustering and text classification.

Development of textual analytics tools for analysis of public and specialized sources in the tasks of foresight and system analysis

A combined approach to extracting concepts and constructing classifiers and ontologies using open and proprietary software packages has been developed. Modern approaches, methods and models of storing large amounts of poorly structured information from Open Source software sets are studied. An ontology was built, in the leaves of which a classifier based on Boolean rules was implemented using SAS(R) Content Categorization Software. To build the ontology, the approach of constructing vectors of related concepts is employed using the Open Source library of Gensim software, namely the Word2Vec model. A typical algorithm for constructing a classifying ontology has been developed. The results of the research can be used to build an ontology of subject areas, create classification ontologies and mark corpora of texts.

A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR.

Free-text problem descriptions are brief explanations of patient diagnoses and issues, commonly found in problem lists and other prominent areas of the medical record. These compact representations often express complex and nuanced medical conditions, making their semantics challenging to fully capture and standardize. In this study, we describe a framework for transforming free-text problem descriptions into standardized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) models. This approach leverages a combination of domain-specific dependency parsers, Bidirectional Encoder Representations from Transformers (BERT) natural language models, and cui2vec Unified Medical Language System (UMLS) concept vectors to align extracted concepts from free-text problem descriptions into structured FHIR models. A neural network classification model is used to classify thirteen relationship types between concepts, facilitating mapping to the FHIR Condition resource. We use data programming, a weak supervision approach, to eliminate the need for a manually annotated training corpus. Shapley values, a mechanism to quantify contribution, are used to interpret the impact of model features. We found that our methods identified the focus concept, or primary clinical concern of the problem description, with an F1 score of 0.95. Relationships from the focus to other modifying concepts were extracted with an F1 score of 0.90. When classifying relationships, our model achieved a 0.89 weighted average F1 score, enabling accurate mapping of attributes into HL7 FHIR models. We also found that the BERT input representation predominantly contributed to the classifier decision as shown by the Shapley values analysis.

Design of thermal wind sensor with constant power control and wind vector measurement method.

This paper presents a conceptual wind vector detector for measuring the velocity and direction of wind in enclosed or semi-enclosed large spaces. Firstly, a thermal wind sensor with constant power control was manufactured and then used as a wind velocity sensing unit. Secondly, a sensor bracket equipped with three thermal wind sensors was designed, the fluid dynamic response regularity of the measured wind field to the sensor bracket was analyzed using ANSYS Fluent CFD software, and then its structural parameters were optimized to improve measurement accuracy. The sensor bracket was fabricated via 3D printing. Finally, a unique wind vector measurement method was developed for the wind vector detector. Experimental results showed that the measured velocity range of the thermal wind sensor satisfied the requirements of being within 0–15 m/s with an accuracy of ±0.3 m/s, and the wind direction angle range of the wind vector detector was within 0–360° with an accuracy of ±5°. By changing the applied power control value of the thermal wind sensor and structural parameters of the sensor bracket, the measurement range and accuracy of the wind vector detector can be adjusted to suit different applications.

Desakota 2.0: worlding hybrid spaces in the global urban

ABSTRACT In a world where rapid urban expansion is occurring in peri-urban fringes of the Global South, this paper advocates for a foregrounding of urban-rural fringes as sites of urban theorization. Heeding to postcolonial urbanism's call for creative experimentation on building wider and inclusive theoretical terrains of urban scholarship, I proffer desakota 2.0 as a worlding conceptual vector. Drawn from the contextual realities of Java, Indonesia, desakota denotes a spatiality characterized by an interplay of urban and rural ways of living. Reloading and re-deploying the term as desakota 2.0 disrupts the city-centric tendency that treats urban-rural spaces as residual sites of theroization and analysis. Worlding desakota 2.0 becomes a provincialized practice engaging with places through a deeper accounting of contextual histories and socio-spatial dynamics, engendering new conceptual formulations and comparisons across contexts. Using the case of Manila's fringe, I discuss desakota 2.0's deployment and two emergent conceptual formulations.