Word Sequences Research Articles

Use of Muscle Relaxants for Acute Postoperative Pain: A Practical Review.

Skeletal muscle relaxants have emerged as a broad category of analgesic adjuncts, aiming to improve pain relief and reduce opioid reliance. These drugs induce muscle relaxation and reduce muscle spasms, and therefore, they are commonly used in surgical procedures involving muscle manipulation, such as abdominal surgery, breast surgery, and spine surgery. However, their analgesic efficacy and opioid-sparing effects are poorly explored. A scoping review of literature was performed with several electronic databases. We used a search string with a sequence of text words and word variants related to central muscle relaxants, pain management, postoperative pain, and specific muscle relaxants. Review of literature shows significant heterogeneity among the studies in terms of surgical procedures, patient populations, choice of muscle relaxant, and timing and duration of administration, potentially limiting the generalizability of the findings and the ability for pooled analysis. Given the lack of evidence, we recommend that the use of skeletal muscle relaxants be reserved for patients in whom an optimal multimodal analgesic technique is not adequate. Also, there may be a limited role for these drugs in patients at high risk of postoperative pain undergoing surgical procedures with expected high opioid requirements. Due to the concerns of potential adverse effects, the decision to use muscle relaxants in vulnerable populations should be made carefully, weighing the benefits against the risks.

اسلوب التجزئة في شعر ما بعد الحداثة الانكليزي: مقال مراجعة موضوع

This paper is a subject review. It investigates the use of the Fragmentation technique in Post-modern English Literature. The paper, meanwhile, examines the different opinions and researches by writers in the post-modern contemporary world. The fragmentation technique points out to a way of writing by using discontinuous sequences of words. It refers, to a condition of illusion and meaninglessness which surrounds the post-modern society.

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

In the existing deep learning sentiment analysis methods, text is directly processed into a sequence of words, which often ignores the important components of the text and the topic of the text. To solve these problems, the topic of text is extracted through Latent Dirichlet Allocation model, and then a method for integrating topic information into neural networks is proposed. Furthermore, an emotion classification model that integrates the topic information is constructed. An attention mechanism is used at the lexical level and the sentence level, paying attention to the more and less important content separately when building the document presentation. The results show that the proposed model has excellent performance in all four kinds of data, among which the accuracy rate, recall rate and F1-score of CCF-BDC are the best, reaching 0.63, 0.72 and 0.7 respectively. The translation accuracy also reaches 0.85. These results show that the model proposed in this study has better performance in sentiment analysis. These research results will help improve the user's reading experience and make machine translation better meet the needs of human language expression.

Phonological neighbors cooperate during spoken-sentence processing: Evidence from a nonword detection task.

We used a novel nonword detection task to examine the lexical competition principle postulated in most models of spoken word recognition. To do so, in Experiment 1 we presented sequences of spoken words with half of the sequences containing a nonword, and the target nonword (i.e., press a response key whenever you detect a nonword in the sequence) could either be phonologically related (a phonological neighbor) or unrelated to the immediately preceding word. We reasoned that the reactivation of a phonological neighbor during target nonword processing should delay the moment at which a nonword decision can be made. Contrary to our hypothesis, participants were faster at detecting nonwords when they were preceded by a phonological neighbor compared with an unrelated word. In Experiment 2, an inhibitory effect of phonological relatedness on nonword decisions was observed in a classic priming situation using the same set of related and unrelated word-nonword pairs. We discuss the implications of these findings in regard to the main models of spoken word recognition, and conclude that our specific experimental set-up with phonological neighbors embedded in spoken sentences is more sensitive to cooperative interactions between co-activated sublexical representations than lexical competition between co-activated lexical representations, with the latter being modulated by whether or not the words compete for the same slot in time.

Detecting hallucinations in large language models using semantic entropy

Large language model (LLM) systems, such as ChatGPT1 or Gemini2, can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers3,4. Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems including fabrication of legal precedents5 or untrue facts in news articles6 and even posing a risk to human life in medical domains such as radiology7. Encouraging truthfulness through supervision or reinforcement has been only partially successful8. Researchers need a general method for detecting hallucinations in LLMs that works even with new and unseen questions to which humans might not know the answer. Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations—confabulations—which are arbitrary and incorrect generations. Our method addresses the fact that one idea can be expressed in many ways by computing uncertainty at the level of meaning rather than specific sequences of words. Our method works across datasets and tasks without a priori knowledge of the task, requires no task-specific data and robustly generalizes to new tasks not seen before. By detecting when a prompt is likely to produce a confabulation, our method helps users understand when they must take extra care with LLMs and opens up new possibilities for using LLMs that are otherwise prevented by their unreliability.

Cryptosystems based on isomorphic transformations of elliptic curve points.

The article presents research in the field of development and improvement of cryptographic systems based on elliptic curves isomorphic transformations potentially resistant to quantum cryptanalysis. Analysis results of existing asymmetric cryptosystems disadvantages and advantages, including those based on isomorphic transformations, are presented. The approaches to the construction of cryptographic algorithms based on isogenies of elliptic curves, which can become the basis for creating cryptosystems resistant to quantum attacks, are investigated. In the course of the research, program functions were developed to implement operations on elliptic curves isogenies of different orders, which will ensure the security levels specified in the standard: 256, 384, 512. A software implementation of the operations of a curve point scalar multiplication and operations on elliptic curve isogenies has been developed, on the basis of which experimental values of the time to perform the scalar product using parallelization have been obtained. Experiments have been conducted to compare the classical multiplication of a curve point with the representation of the scalar k as a sequence of 4-bit words, which made it possible to speed up the scalar multiplication operation by 30 times, for 8-bit words the speedup was 18.8 times. The direction of further research is the development of methods for the generation and verification of a digital signature, based on transformations over the isogeny points of the elliptic curve using the parallelization of operations of scalar multiplication of the curve point.

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

This paper introduces a novel natural language processing (NLP) model as an original approach to sentiment analysis, with a focus on understanding emotional responses during major disasters or conflicts. The model was created specifically for Croatian and is based on unigrams, but it can be used with any language that supports the n-gram model and expanded to multiple word sequences. The presented model generates a sentiment score aligned with discrete and dimensional emotion models, reliability metrics, and individual word scores using affective datasets Extended ANEW and NRC WordEmotion Association Lexicon. The sentiment analysis model incorporates different methodologies, including lexicon-based, machine learning, and hybrid approaches. The process of preprocessing includes translation, lemmatization, and data refinement, utilized automated translation services as well as the CLARIN Knowledge Centre for South Slavic languages (CLASSLA) library, with a particular emphasis on diacritical mark correction and tokenization. The presented model was experimentally evaluated on three simultaneous major natural crises that recently affected Croatia. The study’s findings reveal a significant shift in emotional dimensions during the COVID-19 pandemic, particularly a decrease in valence, arousal, and dominance, which corresponded with the two-month recovery period. Furthermore, the 2020 Croatian earthquakes elicited a wide range of negative discrete emotions, including anger, fear, and sadness, with the recuperation period much longer than in the case of COVID-19. This study represents an advancement in sentiment analysis, particularly in linguistically specific contexts, and provides insights into the emotional landscape shaped by major societal events.

APPLICATION OF HYBRID APPROACH FOR WOLAITA LANGUAGE PART OF SPEECH TAGGING

The main purpose of this study is to develop part-of-speech tagger for Wolaita Language using hybrid approach. Part of speech tagger is one of the subtasks in NLP application which is important for other Natural Language Processing (NLP) applications, like parser, machine translator, speech recognizer and search engines. PoST is a process of tagging a corresponding part of speech tag for a word that tag defines how the word is used in a sentence. The PoST for Wolaita language is not enough yet to be used as one vital module in other natural language processing applications. In this study, the development of PoS tagger using hybrid approach that combines HMM and rule based approaches was conducted for Wolaita language. In general HMM model need large data to increase the performance and the rule based model learner rule based on the language features. The HMM tagger, tags the words based on the optimal path for a given sequence of words and transformation based learning (TBL) is a rule based approaches that learns rule directly from the training corpus without expert knowledge. The developed hybrid approach of Wolaita language PoS tagger uses HMM tagger as initial annotators and rule based tagger as a corrector based on fixed threshold values. For implementation and experiment purpose the researcher used python programming and NLTK. For training and testing the models, 1256 sentences or 15,268 words are collected from three different categories (Bible, Social media in Wolaita language (Wogetta FM 96.6 ) and Wolaita language department) and annotated data manually. For tagging purpose 26 PoS tag are identified. From entire corpus, 90% for training and the remaining of entire corpus for testing purpose. The performance of the taggers, are tested by using different experiments. After experiment the researcher found that the performance of HMM, rule based and hybrid taggers shows 88.14%, 93.19% and 94.82% respectively. Generally, hybrid approach showed the better performance to assigning part of speech tag for Wolaita language.

‘There’s far too much arty-farty, namby-pamby, hoity-toity, wishy-washy, lardy-dardy, sun-dried tomato eating, decaffeinated fannying about!': Reduplicative compounds and other iterative sequences in English

ABSTRACT The aim of the paper is to (re-)evaluate the treatment of reduplicative expressions as compound words in nearly all previous work on English. Examples of reduplicative expressions are listed, out of context, and they are labelled as compound words, without providing proper motivations as to why it makes sense to treat them as compounds, in the sense of lexical items that are formed of other lexical items. The paper provides plenty of examples of how the reduplicatives listed in previous work are actually used in present-day English and examines the functions and interpretations of such expressions. The paper shows that most reduplicative expressions are not compound words. Some of them are words in their own right that cannot be divided into meaningful constituents. Some may have a more compound-like reading, alongside a clearly non-compound-like reading; for most such items, the latter function and interpretation seem to be most common. A large majority of alleged reduplicative compounds display clear non-compound-like behaviour. It will be shown that these expressions are likely to be sequences of words that are created by the same (syntactic) process/es that iterate or stack ‘ordinary’ nouns, adjectives and verbs in English.

N-Gram based Assamese Question Pattern Extraction and Probabilistic Modelling

N-gram probabilities provide valuable information in understanding, processing, and modelling various natural language processing tasks. They assign probabilities to the sequences of words and subsequently to the whole sentence. Such information is very essential to make more accurate predictions in machine learning based systems. Here in this paper we worked on finding Parts-of-Speech (PoS) sequence based Assamese question patterns. We derived the unique bi-grams and tri-grams of PoSs occurring in the patterns and also extracted the probabilities of them. We then tried to find the unique PoS patterns of Assamese questions. We also have tried to incorporate the probabilities of unique bi-grams and tri-grams and the combined bi-grams and tri-grams probabilities of all patterns. Our work is a novel approach of finding the probabilities of bi-grams and tri-grams of the patterns occurring in Assamese questions.

ADVANCMENTS IN TEXT SUMMARIZATION AND EXTRACTIVE QUESTION- ANSWERING : A MACHINE LEARNING APPROACH

In the era of social media platforms, the rapid expansion of data mining in the fields of information retrieval and natural language processing emphasizes the crucial need for automated text summarization. At the current time, pretrained word embedding techniques and sequence to sequence models can be effectively repurposed in the realm of social network summarization to efficiently condense significant information with strong encoding capabilities. However, dealing with the challenge of extended text dependency and efficiently utilizing latent topic mapping presents an increasingly significant obstacle for these computational models. In this document, we propose a topic- focused approach for both extractive and abstractive summarization, integrating Question Answering features by employing BERT and Pegasus pretrained models. A comprehensive analysis of the architectural complexities of the pre-existing models utilized in the creation of our model has been presented. The evaluation of our Text Summarization & Question Answering Model was conducted meticulously on a variety of datasets including Multi news, XSum, and CNN/Daily Mail, with experimental results indicating its achievement of state-of-the-art performance levels based on ROGUE scores. Following this, the verification of our results through human evaluation confirms that the summaries generated by our model are in alignment with the performance standards set by humans across various datasets. Furthermore, we have integrated an Extractive Question Answering task into our model, proposing an architecture leveraging BERT and conducting comparative analyses against alternative language models to gauge its efficacy. Keywords—Natural Language Processing (NLP), Text Summarization, Question & Answering (Q&A), Transformer Models, Pegasus, BERT (Bidirectional Encoder Representations from Transformers), Neural Topic Modeling, Extractive Summarization, Abstractive Summarization, Semantic Representation, Topic Embedding

Examining the criterion validity of curriculum-based measurement of writing (CBM-W) with young EFL learners

This study examines the criterion validity of curriculum-based measurement of writing (CBM-W) for assessing the writing of young language learners in an EFL context. CBM-W has been found to have criterion validity with a variety of tests and student populations, including learners in ESL settings taking standardized tests. However, evidence of CBM-W's validity for use with young learners in EFL settings is limited. This study seeks to address this gap by investigating the criterion validity argument for the following CBM-W scoring procedures: words written (WW), words spelled correctly (WSC), correct word sequences (CWS), and correct minus incorrect word sequences (CIWS). The data set included classroom-administered narrative prompt writing samples and official test scores from the Cambridge Assessment English Young Learners exam reading and writing test collected from 51 Taiwanese English learners aged seven to eight. A Bayesian correlation data analysis found a greater probability favoring the criterion validity argument for CWS and WSC, with strong evidence for CWS and moderate evidence for WSC. This study sets the foundation for future research examining the validity argument of CBM-W for evaluating young EFL learners’ writing in research and classroom settings.

Audley End Annotations

Abstract: This study investigates the writership of a series of 16th century annotations found in at least six books in the library of Audley End House in Essex, UK, which are of possibly historic significance on account of their alleged connections of plot and theme to several of Shakespeare’s plays, especially Julius Caesar (c. 1599) and Antony and Cleopatra (c. 1601). Audley End is a storied mansion, rebuilt by Thomas Howard, 1st Earl of Suffolk, c. 1610 for the express purpose of entertaining King James on his annual progress of English Great Houses. Today operated by English Heritage, the site preserves an impressive library with at least 128 books predating 1604. In 2016, John Casson and William Rubinstein proposed that the annotator of a number of these volumes was Sir Henry Neville (1564-1616), an early owner of the Billingbear estate in Berkshire from which some of the oldest books now at Audley End were evidently transferred circa 1920-1924. If Neville was, in fact, the author of the annotations, would this belief hold up under scrutiny if the principles of forensic document examination were applied? If not, is there another contemporaneous writer whose control sample is a better fit? To make this determination, eight of the 21 “Distinguishing Elements of Handwriting” identified in Huber and Headrick classic text Handwriting Identification: Facts and Fundamentals (1999), were applied. These eight, as numbered by Harrelson and Huber (2018) include Class of Allograph (6.2.1.2), Allograph Combinations (6.2.1.4.5), Design and Construction of Allographs (6.2.1.4), Diacritics (6.3.4), Numerals and Textual Symbols (7.2.1), Connections and Terminations (6.2.1.3), Consistency and Natural Variation (6.4.1), and Embellishments (6.3.5). An additional standard, defined as Whole Word or Letter Sequence Comparison, supplies insight into at least two further discriminating elements, Connections (6.2.1.3), and Spacing (6.2.1.7), as well as affording a larger field of reference than do discriminating elements of single characters. Bold-face words are in the glossary. After applying these standards to the writing of Sir Henry Neville and finding that Neville’s writing contains many unexplained differences from the Audley End Unknown hand, the article then considers and tests the alternative hypothesis that Edward de Vere, 17th Earl of Oxford (1550-1604), is the writer of the question document annotations. After a thorough investigation applying the same standards used to test the hypothesis of Neville’s writership, the evidence instead supports the conclusion that de Vere is a more plausible writer of the Audley End annotations. Purchase Article - $15

Research Of Singing With Note-name Recognition Algorithm Based On End-to-End Model

With the rapid development of artificial intelligence, and people are not only seeking material needs, but also pursuing happiness and spiritual aspirations. Especially some singing talent shows appear, but there are many controversial results. In order to solve the problem of singing with note-name evaluation and evaluate the singer's singing level in the professional music education of solfeggio and ear training, the paper uses artificial intelligence algorithms to recognize the note-name. Firstly, preprocess the singing sound data, including pre emphasis, framing, and windowing to reduce the impact of noise, and extract the acoustic features of the singing sound, the acoustic speech features are organized into a form suitable for DCNN input and a DCNN model is designed to obtain the pinyin form of the singing sound, the softmax layer adopts an end-to-end CTC structure, which classifies the input singing speech, finds corresponding phoneme sequences, and obtains the output results. The end-to-end CTC structure is used to classify and optimize the recognition process, and finally the state of the obtained features is output. And then singing pronunciation dictionary generates candidate word sequences based on the mapping relationship between phonemes and notes. Finally, based on the acoustic model score, the candidate note sequence with the highest score is obtained through decoder processing, and obtained the singing with note-name result of the singing sound. In order to further improve recognition performance, the paper introduces a new CTC-DCNN acoustic model. In this model, residual blocks can transfer input features to the output part through shortcuts, allowing the multi-layer convolutional speech features to be preserved as much as possible. At the same time, the deep structure can also better achieve the extraction and analysis of speech features. A new and improved CTC-DCNN acoustic model is obtained by optimizing the maxout function. The algorithm proposed in this paper is fair and equal in obtaining information, and no one participates in the entire process of obtaining scores. It is believed that the scoring results obtained in this way should be more objective.

Exploring part of speech (pos) tag sequences in a large-scale learner corpus of L2 English: a developmental perspective

This research explores the pos tag sequences that shape the transition from upper intermediate (B2 cefr) to near-native proficiency (C2 cefr) in a corpus of essays ( n=32,410) from the Cambridge Learner Corpus. Gilquin (2018) and others have shown that pos tag sequences offer a holistic approach to extracting the most commonly used patterns without a starting point of an a priori set of words and word sequences. Using corpus linguistics informed by usage-based theories of language learning, this paper examines the frequency and distribution of 4-slot pos-tag sequences in L2 English writing, drawing on the taxonomy of pattern grammar ( Francis et al., 1996 , 1998 ; and Hunston and Francis, 2000 ). Findings point to the presence of both core and emergent pos-tag sequences in learner language in the two proficiency levels analysed. These sequences point to the presence of dynamic language restructuring processes as learners become more proficient and re-evaluate their understanding of frequency and distribution in English. This paper shows evidence of how language competence increases with proficiency. The research offers new evidence in our understanding of the development of L2 writing in efl contexts.

Talker change detection by listeners varying in age and hearing loss.

Despite a vast literature on how speech intelligibility is affected by hearing loss and advanced age, remarkably little is known about the perception of talker-related information in these populations. Here, we assessed the ability of listeners to detect whether a change in talker occurred while listening to and identifying sentence-length sequences of words. Participants were recruited in four groups that differed in their age (younger/older) and hearing status (normal/impaired). The task was conducted in quiet or in a background of same-sex two-talker speech babble. We found that age and hearing loss had detrimental effects on talker change detection, in addition to their expected effects on word recognition. We also found subtle differences in the effects of age and hearing loss for trials in which the talker changed vs trials in which the talker did not change. These findings suggest that part of the difficulty encountered by older listeners, and by listeners with hearing loss, when communicating in group situations, may be due to a reduced ability to identify and discriminate between the participants in the conversation.

Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars.

In computational neurolinguistics, it has been demonstrated that hierarchical models such as recurrent neural network grammars (RNNGs), which jointly generate word sequences and their syntactic structures via the syntactic composition, better explained human brain activity than sequential models such as long short-term memory networks (LSTMs). However, the vanilla RNNG has employed the top-down parsing strategy, which has been pointed out in the psycholinguistics literature as suboptimal especially for head-final/left-branching languages, and alternatively the left-corner parsing strategy has been proposed as the psychologically plausible parsing strategy. In this article, building on this line of inquiry, we investigate not only whether hierarchical models like RNNGs better explain human brain activity than sequential models like LSTMs, but also which parsing strategy is more neurobiologically plausible, by developing a novel fMRI corpus where participants read newspaper articles in a head-final/left-branching language, namely Japanese, through the naturalistic fMRI experiment. The results revealed that left-corner RNNGs outperformed both LSTMs and top-down RNNGs in the left inferior frontal and temporal-parietal regions, suggesting that there are certain brain regions that localize the syntactic composition with the left-corner parsing strategy.

Writing productivity development in elementary school: A systematic review

The ability to produce fluent and coherent written text impacts learning and attainments. Valid and reliable assessments of writing are needed to monitor progression, develop goals for writing and identify struggling writers. In order to inform practice and research a systematic review was conducted to investigate which writing productivity measures captured writing development and identified struggling writers in elementary school. Sixty-seven empirical studies were identified for inclusion, appraised, and their data extracted under the themes of writing genre, duration of writing task, use of priming of topic knowledge prior to the writing assessment, use of planning time, writing modality, gender, age of participants and learning difficulties. Total Number of Words and Correct Word Sequences were the most common means of measuring productivity. Productivity varied significantly between genres and durations of writing tasks and was higher in girls than boys. Students with learning difficulties scored significantly lower in writing productivity when compared to typically developing peers. Insufficient research was available to draw conclusions regarding the effects of priming of topic knowledge, planning and modality on writing productivity. Study limitations, links to the assessment of writing and recommended further research are discussed.

CHANGES IN THE USAGE OF LEXICAL BUNDLES IN THE FIELD OF EDUCATION DURING THE LAST 20 YEARS

Formulaic language contributes to demonstrating membership in a specific discourse community. However, the norms of a specific language community tend to change over time, which adds to the complexity of academic writing. Therefore, the aim of this paper is to explore the changes in the usage of lexical bundles in scientific papers in the field of education over the last 20 years. The focus is on lexical bundles, multi word sequences that recur frequently and are distributed widely across different texts. As a prominent feature of any text, exploring their usage and change over time brings insight into the changes which occur in the academic writing style. The results showed a decrease in the usage of lexical bundles over time. Statistically significant differences were found in the structural and functional usage of lexical bundle tokens for both groups and subgroups. This indicates that the concrete bundles used over time have not significantly changed but only the frequency of their usage.

Sensor event sequence prediction for proactive smart home: A GPT2-based autoregressive language model approach

We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents’ activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%.