Unique Word Research Articles

Sarcasm Detection for Hindi English Code Mixed Twitter Data

Abstract: Twitter, Instagram have grown to be the two most popular social media sites for users to voice their opinions on a variety of subjects. The generation of such huge amounts of user data has made NLP activities like sentiment analysis and opinion mining far more important. Sarcastic statements on social media,also referred to as memes, have recently become very popular. Sarcasm challenges many NLP tasks because it flips the meaning and polarity of what the language implies. A lot of resources were developed for the English language, but this does not hold true for Hinglish In this paper we include a tweet corpus for training unique word embeddings as well as a Hinglish dataset labelled forsarcasm detection. Although there have been various attempts to categorise a text’s sentiment, there aren’t many models that can do the same when given non-English data that contains sarcasm or irony . This study compares numerous sarcasm detection methods for Hinglish data in order to determine which approach performs the best on datasets of various sizes and types. We have presented a technique that will enhance the outcomes of sarcasm recognition for Hindi- English code-mixed tweets by examining and researching the prior work.

Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining

In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets.

Automated Handwritten Text Recognition

A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.

Emotion word use patterns and eating disorder symptoms: Considering the circumplex model of affect and basic emotions theory.

No prior research has examined whether the types of emotion words individuals use to describe their affective experiences cluster along affective dimensions inherent within leading affect theories, or how such emotion word use maps onto eating disorder (ED) symptoms. To address these gaps, latent profile analysis was used to empirically-identify groups of young adults (N=352) by how often they use emotion words characterized by the circumplex model of affect's valence-arousal dimensions and basic emotions theory's basic versus complex emotion word categorizations. Auxiliary analyses examined differences in groups' ED symptoms (binge eating, purging, restricting, excessive exercising, muscle building, body dissatisfaction, and cognitive restraint). The 5-profile valence-arousal model and 4-profile basic-complex model were the best-fitting theoretically-supported solutions. Valence-arousal profiles with greater negative affect valence generally exhibited worse ED pathology than others, whereas profiles with greater positive affect valence produced inconsistent risk- and protective-factor relations with distinct ED symptoms. Basic-complex profiles characterized by frequent use of both basic and complex emotion words generally had the greatest ED severity, and profiles with greater basic emotion word use exhibited elevated binge eating. Individual-differences in young adults' emotion word use patterns, versus sample-level averages only, warrant further consideration in ED prevention and research. The present findings suggest that young adults differ in the types of words they use to describe their emotional experiences, and that these unique emotion word use patterns are linked to distinct eating disorder symptoms. These sources of variation warrant further consideration in eating disorders prevention efforts and future research seeking to advance affect-based eating disorders theories.

BER Reduction and Capacity Enhancement with Novel Guard Sequence Selection for Multi-Carrier Communication

Orthogonal frequency division multiplexing (OFDM) is an efficient multicarrier scheme that uses different types of guard intervals such as cyclic prefix (CP) and known symbol padding (KSP) (zero padding (ZP), unique word (UW), etc.) in block formation. Among these guard intervals, CP varies for each block, while other guard intervals remain fixed from block to block. These guard intervals efficiently perform channel estimation, synchronization and remove inter-block interference (IBI); nevertheless, none of the existing schemes develop any relationship between the guard interval (sequence) and the data symbols on different subcarriers of the OFDM block. We present a new idea of selecting the guard interval based on the data symbols of a subset of subcarriers in the block and exploit the high auto-correlation of the selected guard sequence to improve the bit error rate (BER) performance of the system. The results based on a fair comparison show that our enhanced orthogonal frequency division multiplexing (eOFDM) scheme inherits significant improvements in BER and the capacity of a multicarrier system as compared to the existing techniques.

Growth in eligibility criteria content and failure to accrue among National Cancer Institute (NCI)-affiliated clinical trials.

Cancer trial accrual is a national priority, yet up to 20% of trials fail to accrue. Trial eligibility criteria growth may be associated with accrual failure. We sought to quantify eligibility criteria growth within National Cancer Institute (NCI)-affiliated trials and determine impact on accrual. Utilizing the Aggregated Analysis of ClinicalTrials.gov, we analyzed phase II/III interventional NCI-affiliated trials initiated between 2008 and 2018. Eligibility criteria growth was assessed via number of unique content words within combined inclusion and exclusion criteria. Association between unique word count and accrual failure was evaluated with multivariable logistic regression, adjusting for known predictors of failure. Medical terms associated with accrual failure were identified via natural language processing and categorized. Of 1197 trials, 231 (19.3%) failed due to low accrual. Accrual failure rate increased with eligibility criteria growth, from 11.8% in the lowest decile (12-112 words) to 29.4% in the highest decile (445-750 words). Median eligibility criteria increased over time, from 214 (IQR [23, 282]) unique content words in 2008 to 417 (IQR [289, 514]) in 2018 (r2 =0.73, P < 0.001). Eligibility criteria growth was independently associated with accrual failure (OR: 1.09 per decile, 95% CI [1.03-1.15], p=0.004). Eighteen exclusion criteria categories were significantly associated with accrual failure, including renal, pulmonary, and diabetic, among others (Bonferroni-corrected p < 0.001). Eligibility criteria content growth is increasing dramatically among NCI-affiliated trials and is strongly associated with accrual failure. These findings support national initiatives to simplify eligibility criteria and suggest that further efforts are warranted to improve cancer trial accrual.

ON A TOPOLOGY DEFINED BY PRIMITIVE WORDS

Let f:X⟶X be a mapping. Consider P(f)={O⊆X:f-1(O)⊆O}. Then P(f) is an Alexandroff topology. A topological space X is called a primal space if its topology coincides with a P(f) for some mapping f:X⟶X. Let A be an alphabet and A* be the set of all finite words over A. A word is called primitive if it is not empty and not a proper power of another word. Let u be a nonempty word; then there exists a unique primitive word z and a unique integer k≥1 such that u=zk; z is called the primitive root of u; we denote by z=pA(u). It is convenient to set pA(εA)=εA, where εA is the empty word over A. By a primitive primal space we mean a space X that is homeomorphic to the subspace A+ of A* equipped with the topology P(pA) for some alphabet A. Our main result provides a structure theorem of primitive primal spaces.

Bryusov-lyrics' Genre in a Translation Dialogue with French Symbolism

The subject of the study is an intertextual dialogue between V.Y. Bryusov and Paul Verlaine based on the poem "Croquis parisienne" ("Parizhskoe croquis"). The object of the research is the ways and means of forming intertextual and intercultural dialogue in poetic translations of the Silver Age. The authors consider in detail such aspects of the topic as the unique word usage of the word "croquis" in the use of V.Y. Bryusov, reinforcing the conclusions with quotations from the National Corpus of the Russian language. Special attention is paid to the exclusive use of the word "croquis" as a genre mark, which corresponds to the extensive tactics of mastering genres by V.Y. Bryusov both in translations and in original poetry. The main conclusions of the study are the means allocated by the authors to implement intertextual dialogue in translation: the changes made by the translator to the original text strengthen the dialogic component of the picture developed by the author and weaken the motive of existential loneliness presented in the original text. A special contribution of the authors to the study of the topic is the identification in the translation work of V.Y. Bryusov of the original genre of the ekphrasic type - "croquis"-a sketch associated with the visual and verbal representation of the image. The novelty of the research lies in a detailed description of this impressionistic poetic genre and tracing its semantic and aesthetic connections with the fundamental attitudes of Impressionism in painting.

CUNA: A privacy preserving medical records storage in cloud environment using deep encryption

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.

Bridging Conversation Islands to Connect Healthcare: Introducing Unique Co-occurring Word Networks to Find Distinct Themes

A common step in any modeling, study, or design project is gathering, reviewing, and making sense of relevant information. Common themes and variation are unearthed from text documents and shaped into usable forms to support the information needs of the project team. It can be a challenge to gather needed information from large numbers of texts and documents necessary to inform design decisions. Computational approaches can be useful in filtering and interpreting information. Unsupervised learning approaches like topic modeling can help to group similar texts and visualize potential themes. Just like grouping texts into similar themes, identifying distinctions can be helpful. This paper describes an approach for finding distinctions across texts and examples of using this information to inform decisions. Finding distinct themes can help to identify gaps and opportunities while guiding decisions and next steps. Identifying and bridging conversation islands across information sources and professions is another step towards managing complexity and connecting the dots of healthcare.

THE RELATIONSHIP OF THE LITERAL MÜTESÂBIHÂT IN THE QUR'AN AND THE QURAN'S İ‘

Kur’ân’ın ilâhî kaynaklı olduğunun en büyük delili şüphesiz kendisidir. Mana yönünden sahip olduğu pek çok özelliğiyle benzersiz bir söz olan Kur’ân’ın pek çok i’câz veçhi, lafızlarındaki inceliklerde saklıdır. Ne var ki Kur’ân’ın mucizevî yönüne iman etmek ile ona vakıf olmak arasında fark vardır. Bütün müminler, Kur’ân’ın ilâhî menşeli olduğuna iman ederken i’caz özelliklerinden hareketle ilâhîliğini ortaya koyamazlar. Kur’ân’ın benzersiz bir yapıya sahip olduğunun ortaya konulması, başta samimiyet olmak üzere ilmî yeterliliğe, geniş bir perspektife, duru ve parlak bir zekâya sahip olmanın yanında fıtrî bir yeteneğe de ihtiyaç vardır. Bu çalışmada, mülhitler tarafından Kur’ân’ın karalanması için ileri sürülen bahanelerden biri olan lafzî müteşâbihâtın, ehil isimlerin nazarında Kur’ân’ın beyânî i’cazının önemli örnekleri olduğu ortaya konulmaktadır. Kur’ân’daki bazı lafzî müteşâbihlerin incelenmesinin hedeflendiği bu çalışmada doküman analizi metoduna başvurulacaktır

Contrasting the primings between English and Chinese: To advance Hoey's theory of lexical priming from the perspective of culture psychology.

The present study demonstrated that Hoey's promising and serviceable theory could serve as one that can describe and explain similarities and differences exhibited between English and Chinese. Three fundamental concepts as defined in the theory (collocation, colligation, and semantic association) were considered. The combinatory profile showed that: (1) different word choices were primed to constitute to a shared semantic association (2) no unique word combination containing the nodes was found, but high or low frequency use of particular clusters and collocates appearing at both sides to the node occurred; (3) it is claimed that the interpretation and explanation of priming similarities and differences between English and Chinese need to be extended to culture psychology.

Joint Frame Detection and Channel Parameter Estimation for OOK Free-Space Optical Communications

We consider a free-space optical (FSO) communication link for packet-based transmissions over a turbulence fading channel. The modulation format is on-off keying (OOK) and a unique-word (UW) composed by a known synch pattern is periodically inserted in the data stream to identify the start of frame. Since an avalanche photo-diode (APD) is used for direct detection of the OOK symbols, the photocurrent signal provided by the APD is plagued by a mixture of thermal and shot noise with signal-dependent power. Our goal is the detection of the UW position in the received stream, along with the estimation of the unknown channel attenuation and noise variances. The aforementioned problem has recently been studied by ignoring any information conveyed by data symbols surrounding the UW. In this work, further investigation is conducted in order to determine the maximum likelihood (ML) solution that, in addition to the UW, exploits all the information-bearing symbols belonging to the observation window. The relevant Cramér-Rao bound (CRB) is also evaluated to establish the ultimate accuracy achievable in the estimation process. Since the true ML estimator leads to a computationally intractable multi-dimensional optimization problem, we develop suitable approximations that enable accurate frame synchronization with affordable complexity. We also present an iterative solution to refine the channel and noise power estimates provided by the UW detection procedure. Numerical simulations demonstrate the superiority of the proposed synchronization and estimation schemes with respect to existing alternatives.

Multimedisation of Contemporary Art in the Context of Globalisation and European Integration

Modernity has given humanity such a quality of public life as globalisation in its various spheres. As the common root of this concept with the word “globe” suggests, it refers to an object that unites the whole world, including its most remote, unexplored corners into a single whole. The positive side of this phenomenon is the universal involvement in progressive trends in the history (meaning its modern stage) of mankind, the opportunity to acquire sources of fresh knowledge about the world, improvement of the quality of being in general. This also applies to such a sphere of consciousness as art, in particular, to its samples that emerged in the late 20th and early 21st centuries. As is known, it absorbed the whole huge complex of achievements of the cultural fund of previous eras, integrated various, sometimes very distant artistic traditions, schools, and their stylistic orientation into a single whole, also bringing its own new and unique word – extraordinary complexity, ambiguity, paradoxicity, and unpredictability of content. The energy of the experiment penetrates into the sphere of contemporary art as its integral quality, the most important component. In this regard, the interaction of artistic creativity with engineering science, including the latest achievements of information and communication technology software, becomes logically conditioned and natural. They are also designed to provide considerable assistance to the authors of works of modern culture in terms of the updated design of ideas and their presentation to the audience in a unique innovative format. Thus, the problem of studying the multimedisation of contemporary art in the context of globalisation and European integration becomes natural and urgent.

Deep Learning for Chinese Language Sentiment Extraction and Analysis

In recent years, vocabulary emotion processing has become immensely popular and the requirements for language emotion analysis mining and processing have become significantly abundant. The sentiment extraction and analysis work has always been very challenging; especially, the Chinese word segmentation operation is difficult to deal with effectively, the multiple combinations of implicit and explicit words make the task of sentiment analysis mining more difficult, and, in particular, the efficiency of machine analysis of language sentiment is feeble. We use some expressions and sentiment vocabulary dictionaries combined with hybrid structures and use information synergy methods to get in touch with sentiment analysis methods. We use the relevant sentiment to evaluate the explicit or implicit emotional association of the emotional connection of the vocabulary and add the unique emotional word matrix to analyze the related clustering results of the emotional words to continuously optimize and upgrade the performance, so that our sentiment analysis results are systematic in terms of efficiency and significantly improved.

Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF.

The Internet is rich in information related to the financial field. The financial entity information text containing new internet vocabulary has a certain impact on the results of existing recognition algorithms. How to solve the problems of new vocabulary and polysemy is a problem to be solved in the current field. This paper proposes an ERNIE-Doc-BiLSTM-CRF named entity recognition model based on the pretrained language model. Compared with the traditional model, the ERNIE-Doc pretrained language model constructs a unique word vector from the word vector and combines the location coding, which solves polysemy problem well. The intensive skimming mechanism realizes the long text processing well and captures the context information effectively. The experimental results show that the accuracy of this model is 86.72%, the recall rate is 83.39%, and the F1 value is 85.02%, which is 13.36% higher than other models; the recall rate is increased by 13.05%, and the F1 value is increased by 13.21%.

A New Approach for Explicit Distributional Semantic Vector Representation Based on Dependency Distance and Bias Elimination

We consider the problem of the semantic representation of words based on explicit basis word vectors. We focus on explicit semantic vectors in which each basis vector is equivalent to a unique word. In this paper, we propose a new approach to represent semantic vectors explicitly. The approach consists of three steps: First, we introduce two new factors called word similarity (WS) and the number of zeroes (NZ) for choosing informative context words. Second, we propose a dependency distance method by incorporating the syntactic relations of the words in the sentences. This means that the method uses not only semantic information but also syntactic one. Third, we propose two shifting methods based on WF and WS factors to eliminate the bias of the PPMI matrix. In this work, we use the ukWaC corpus to select context words and compute co-occurrence matrices. Then, we examine the results on MEN, RG-65, SimLex-999, and WordSim353 test sets. We report the performance in comparison with a Baseline in which, the word frequency has been used. The Baseline method uses a window to fix the influence of the neighboring words. Our approach improves the Spearman correlation coefficient for MEN, RG-65, SimLex-999, and WordSim353 datasets by 3.87%, 10.25%, 5.04%, and 5.24%, respectively. So, the experiment results show that the proposed context words selection, incorporating dependency distance, and using the WF-based shifting method significantly improve the performance.

Identity Today and the Critical Task of Semioethics

The critical task of semioethics implies recognition of the common condition of dialogical interrelation and the capacity for listening, where dialogue does not imply a relation we choose to concede thanks to a sense of generosity towards the other, but on the contrary is no less than structural to life itself, a necessary condition for life to flourish, an inevitable imposition. With specific reference to anthroposemiosis, semioethics focuses on the concrete singularity of the human individual and the inevitability of intercorporeal interconnection with others. The singularity, uniqueness of each one of us implies otherness and dialogism. Semioethics assumes that whatever the object of study and however specialized the analysis, human individuals in their concrete singularity cannot ignore the inevitable condition of involvement in the destiny of others, that is, involvement without alibis. From this point of view, the symptoms studied from a semioethical perspective are not only specified in their singularity, on the basis of a unique relationship with the other, the world, self, but are above all social symptoms. Any idea, wish, sentiment, value, interest, need, evil or good examined by semioethics as a symptom is expressed in the word, the unique word, the embodied word, in the voice which arises in the dialectic and dialogical interrelation between singularity and sociality.

Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal

In recent years, distributed unique word (DUW) has been widely used in satellite single carrier TDMA signals, such as very small aperture terminal (VSAT) satellite systems. Different from the centralized structure of traditional unique word, DUW is uniformly dispersed in a burst signal, where the traditional unique word detection methods are not applicable anymore. For this, we propose a robust burst detection algorithm based on DUW. Firstly, we allocated the sliding detection windows with the same structures as DUW in order to effectively detect it. Secondly, we adopt the method of time delay conjugate multiplication to eliminate the influence of frequency offset on detection performance. Due to the uniform dispersion of DUW, it naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. So, we divide the traditional dual correlation formula into two parts to calculate them separately and obtain a dual correlation detection algorithm, which is suitable for DUW. Simulation and experimental results demonstrate that when the distribution structure of DUW changes, detection probability of the proposed algorithm fluctuates little, and its variance is 1.56×10−5, which is 99.83% lower than the existing DUW detection algorithms. In addition, its signal to noise ratio (SNR) threshold is about 1 dB lower than the existing algorithms under the same circumstance of the missed detection probability.

Enabling Unique Word OFDM for Underwater Acoustic Communication

This letter presents a new scheme to solve the issue of high redundant energy in Unique Word Orthogonal Frequency Division Multiplexing (UW-OFDM). The proposed scheme, called Low-Redundant Energy UW-OFDM (LRE-UW-OFDM), is suitable for time-varied harsh channels such as the Underwater Acoustic (UWA) channel were inserting the UW data in the time domain leads to huge redundant energy and a dramatic deterioration in Bit Error Rate (BER) performance. The insertion of UW in the proposed LRE-UW-OFDM is done in frequency domain to be any desired sequences in time domain with constant redundant subcarriers energy regardless of the size and the location of data subcarriers. The proposed scheme is evaluated over a long-delay UWA channel demonstrating its outperformance against conventional schemes in terms of BER and mean symbol energy.