Confusion Set Research Articles

Understanding English Rhetorical Strategies Based on Neurosemantic Analysis

Abstract This paper designs and implements a model for analyzing rhetorical strategies of English texts to address practical needs. This paper combines the true word confusion set and the N meta-model to correct word errors. In addition, a discourse parser is constructed based on the theory of rhetorical structure, which analyzes the semantics of the English text from the intra-sentence and inter-sentence perspectives to extract the key information of the discourse. The information is inputted into a memory network that resembles a tree, and the memory units and gating mechanism are employed to obtain semantic structural features. Finally, a hierarchical network model based on the attention mechanism is designed, using a bidirectional long short-term memory network to extract the global dependency information of the text and adding the attention mechanism to make it adaptively select important features related to English rhetorical strategies. Combining learning of a tree-based long short-term memory network and a hierarchical network based on an attention mechanism is used to generate a model for semantic analysis of English text. This paper’s model has the highest accuracy, recall, and F1 value among the mentioned models in the 20newsgroups dataset, with 93.77%, 91.23%, and 90.15%, respectively. The performance on the three rhetorical strategies of explicit metaphor, personification, and analogy is excellent, with classification accuracy above 90%, which will accomplish the task of analyzing and understanding English modification strategies.

Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates

Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Consequently, these methods tend to overcorrect correct words, leading to a high rate of false positives. To address this issue, we researched the specific characteristics of the Chinese grammar error correction (CGEC) task in high-error density situations. We proposed a grammar-correction method based on prompt templates. Firstly, we proposed a strategy for constructing prompt templates suitable for CGEC. This strategy transforms the CGEC task into a masked fill-in-the-blank task compatible with the masked language model BERT. Secondly, we proposed a method for dynamically updating templates, which incorporates already corrected errors into the template through dynamic updates to improve the template quality. Moreover, we used the phonetic and graphical resemblance knowledge from the confusion set as guiding information. By combining this with BERT’s prediction results, the model can more accurately select the correct characters, significantly enhancing the accuracy of the model’s prediction correction results. Our methods were validated through experiments on a public grammar-correction dataset. The results indicate that our method achieves higher correction performance and lower false correction rates in high-error density scenarios.

ABC-Fusion: Adapter-based BERT-level confusion set fusion approach for Chinese spelling correction

Chinese spelling correction (CSC) aims to automatically detect and correct spelling errors in Chinese sentences. Recently, the method that combines a pre-trained language model with external knowledge has achieved excellent performance. The knowledge is either derived from multi-modal information such as pronunciations and glyphs, or from a confusion set that collects confusing character pairs. However, existing advanced multi-modal knowledge based methods have superior performance at the cost of largely increased model size; and although context semantics is essential for CSC, current confusion set based methods fail to use the confusion set to model the semantics as they do not fuse the lexical feature. To deal with these issues, we propose an Adapter-based BERT-level Confusion Set Fusion method which fuses BERT with the semantics of confusing characters in the semantic encoding phase. A lightweight adapter is designed to be placed between BERT layers, which dynamically extracts the relevant knowledge among the confusing candidates and integrates it with the context. In this way, the contextual information and the semantics of the candidates can fully interact within BERT. Experiments11Code are available at https://github.com/jying2023/ABC-Fusion. are conducted on three benchmarks. The results demonstrate that our method outperforms the previous confusion set based methods and shows comparable performance with the multi-modal knowledge based methods.

Newly outbreak of Nipah virus: epidemiology, symptoms, transmission, diagnostic testing, treatment, and global health concern

Newly outbreak of Nipah virus: epidemiology, symptoms, transmission, diagnostic testing, treatment, and global health concern

Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction

Chinese Spelling Check (CSC) aims to detect and correct spelling errors in Chinese. Most CSC models rely on human-defined confusion sets to narrow the search space, failing to resolve errors outside the confusion set. However, most spelling errors in current benchmark datasets are character pairs in similar pronunciations. Errors in similar shapes and errors which are visually and phonologically irrelevant are not considered. Furthermore, widely-used automatically generated training data in CSC tasks leads to label leakage and unfair comparison between different methods. In this work, we propose a feature (visual and phonological) enhanced siamese BERT to (1) correct spelling errors without using confusion sets; (2) integrate phonological and visual features for CSC by a glyph graph; (3) improve performance for unseen spelling errors. To evaluate CSC methods fairly and comprehensively, we build a large-scale CSC dataset in which the number of samples in different error types is the same. The experimental results show that the proposed approach achieves better performance compared with previous state-of-the-art methods on three benchmark datasets and the new error-type balanced dataset.

An Errors Correction Model for the Errors of Non-word and Real-word in English Composition

<p>In the procedure from composing English, it is inevitable to face the phenomenon of word writing errors. In recent years, English composition automatic correcting system has attracted much attention. However, the precision of the existing word errors correcting system is vague generalization. So as to move forward the accuracy of checking and correcting word errors, this paper designs a word errors correction model based on natural language processing technology. This model designs phoneme matching method based on an improved IDM algorithm, and combined with a non-word input errors correction method based on character distance. The accuracy of correcting non-word errors in this model reached 86.5%. The study also proposes a real-word errors correction method, which is implemented basing on the real-word confusion set and combining the binary statistical model and the GloVe word vector model, improving the real-word errors correction method based on feature annotation of the real-word confusion set, with an accuracy of 77.9%. </p> <p> </p>

The toxic power dynamics of gaslighting in medicine.

![Figure][1]</img> Early in the black-and-white movie Gaslight , the protagonist (played by Ingrid Bergman) sits alone in her bedroom. As she stares at a dimming gas-fueled lamp, confusion sets in. Did the light just dim on its own? Much later in the film, she questions this again, but this time

Domain-Shift Conditioning Using Adaptable Filtering Via Hierarchical Embeddings for Robust Chinese Spell Check

Spell check is a useful application which processes noisy human-generated text. Spell check for Chinese poses unresolved problems due to the large number of characters, the sparse distribution of errors, and the dearth of resources with sufficient coverage of heterogeneous and shifting error domains. For Chinese spell check, filtering using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, shifting error domains. We propose a scalable adaptable filter that exploits hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity problems related to infrequent errors. Our approach compares favorably with competitive baselines and obtains SOTA results on the 2014 and 2015 Chinese Spelling Check Bake-off datasets.

Beyond top‐N accuracy indicator: a comprehensive evaluation indicator of CNN models in image classification

Nowadays, a large number of deep convolutional neural network (CNN) models are applied to image classification tasks. However, the authors find that the most widely used evaluation indicator, the Top‐N Accuracy indicator, cannot discriminate these models effectively. In this study, they propose a new indicator called Maximum‐Spanning‐Confusion‐Tree indicator to solve this problem. The Maximum‐Spanning‐Confusion‐Tree indicator is computed based on the hierarchical structure of the Maximum Spanning Confusion Tree of the deep CNN model on the dataset and reflect the ability of deep CNN models to discriminate confused categories in the dataset. The hierarchical structure of the Maximum Spanning Confusion Tree can reveal the confused category set of one selected category in the dataset efficiently and flexibly. Experiments show that they can discriminate ten different deep CNN models more accurately with the Maximum Spanning Confusion Tree indicator than the Top‐N Accuracy indicator and the Maximum Spanning Confusion Tree intuitively shows the distribution of confused category sets in the dataset so they can find out the weakness of deep CNN models effectively.

Real-Word Errors in Arabic Texts: A Better Algorithm for Detection and Correction

Real-word also known as semantic or context-sensitive spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word–by mistake–when another is intended, e.g., “I want a peace piece of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets–a simple approach used by many works tackling this problem–which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram n = 1–3 language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.

Children, Development, and the Troubled Foundations of Miller v. Alabama

While the boundaries between child, adolescent, and adult are difficult to define, there is a consensus that children and adults are different in kind. Extreme acts of violence put pressure on that consensus. Children that kill, for many, create a kind of border problem for juvenile justice. That public opinion tends to align with the general claim that children who break the law should be given a break belies a deeper set of confusions. On what grounds should a seventeen-year-old that kills be treated more leniently than his eighteen-year-old counterpart? For the U.S. Supreme Court majority, the solution is to root its doctrine in a scientifically supported “developmental approach.” This article argues that this approach is philosophically confused. One must abandon, or significantly amend, that dominant understanding to explain how a principled concern with proportional punishment simultaneously justifies and limits the legal response to children that kill. The final pages sketch an alternative account that may be able to address the shortcomings of appeals to development. To punish children as adults, I suggest, is an attempt to reap the benefits of paternalism without bearing the accompanying political, social, and moral costs.

The Refugees We Are: Solidarity in the European Constitutional Imagination

The Refugees We Are: Solidarity in the European Constitutional Imagination

An improved protein structure evaluation using a semi-empirically derived structure property

BackgroundIn the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.ResultIt was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.ConclusionIn this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.

Research and Implementation of Chinese Text Automatic Proofreading System

The news media platform has a huge amount of original news releases every day, it is impractical to use manual review of text typos. This paper designed and implemented a Chinese text automatic proofreading system for large-scale text content and high-speed processing. The proofreading content is first analyzed and classified: typos and sensitive information. Firstly, the system used the n-gram model to statistically analyze the corpus after segmentation to form a 2-gram model library and a contextual context library; secondly, builded a typo confusion set, and then calculated the probability of the target word in the knowledge base to realize automatic error detection and correction of Chinese text. The system has been successfully applied to the error of the content of many government news media platforms, each server can handle one million articles every day. The results show that the recall rate of the article is 78.9% and the accuracy rate is 85.1%. It meets the demand of high speed and accurate processing of massive text error, and has important practical significance and application fields.

From design to action: Papua New Guinea’s latest state-owned enterprise policy

This article addresses policy developments concerning the structure, operation and performance of state-owned enterprises (SOEs) in Papua New Guinea. Of particular significance are reforms since 2014 which have highlighted important tensions between the country’s constitutional imperative of self-reliance and governance initiatives aimed at ensuring SOEs have a considerable degree of operational autonomy suited to their commercial objectives. The result of the reforms is a confused and contradictory set of institutional arrangements that have increased rather than reduced the possibility of direct political control over SOEs. While there are efforts to operate SOEs as efficiently as possible within current constraints, the political will to implement those parts of the reforms that have the greatest likelihood of improving the financial performance of SOEs appears to be lacking.

Technology: The forgotten science

The purpose of this article is to promote the more effective use of technology, not only at the level of technological detail, but also at the level of corporate guidance and national policy. Only a small minority of technology professionals and engineers are mindful today of the true origins of the technology concept. Technology was originally conceived as a body of knowledge organized into a scientific discipline that described purposeful action in industry. Technology management and policy are of the most challenging tasks facing specialists and engineers. This article suggests a new key for grasping technology. New, because the key that defined the science in the first place has been irretrievably lost. The situation is reminiscent of the story of the tower of Babel. According to this narrative a group of builders came together to construct a tower that would reach to the sky. They wished to interact with the master of the universe. The master feared this possibility and decided to interrupt progress. He noted that for a group acting as one people with one language, virtually anything was attainable. His solution: introduce multiple languages so that the builders could not effectively communicate. Confusion set in. The project could not be completed. A similar state of confusion exists in the world of technology today. Different parts of technological knowledge exist independently and side by side- without a unifying key to explain mutual relationships and shared impact. To find a new key it is useful to trace how the original came into being, how it became popular, how it guided technological evolution, and, eventually, how it was lost. It is a little known and fascinating history.

A Seed-Based Method for Generating Chinese Confusion Sets

In natural language, people often misuse a word (called a “confused word”) in place of other words (called “confusing words”). In misspelling corrections, many approaches to finding and correcting misspelling errors are based on a simple notion called a “confusion set.” The confusion set of a confused word consists of confusing words. In this article, we propose a new method of building Chinese character confusion sets. Our method is composed of two major phases. In the first phase, we build a list of seed confusion sets for each Chinese character, which is based on measuring similarity in character pinyin or similarity in character shape. In this phase, all confusion sets are constructed manually, and the confusion sets are organized into a graph, called a “seed confusion graph” (SCG), in which vertices denote characters and edges are pairs of characters in the form (confused character, confusing character). In the second phase, we extend the SCG by acquiring more pairs of (confused character, confusing character) from a large Chinese corpus. For this, we use several word patterns (or patterns) to generate new confusion pairs and then verify the pairs before adding them into a SCG. Comprehensive experiments show that our method of extending confusion sets is effective. Also, we shall use the confusion sets in Chinese misspelling corrections to show the utility of our method.

A Hybrid Ranking Approach to Chinese Spelling Check

We propose a novel framework for Chinese Spelling Check (CSC), which is an automatic algorithm to detect and correct Chinese spelling errors. Our framework contains two key components: candidate generation and candidate ranking . Our framework differs from previous research, such as Statistical Machine Translation (SMT) based model or Language Model (LM) based model, in that we use both SMT and LM models as components of our framework for generating the correction candidates, in order to obtain maximum recall; to improve the precision, we further employ a Support Vector Machines (SVM) classifier to rank the candidates generated by the SMT and the LM. Experiments show that our framework outperforms other systems, which adopted the same or similar resources as ours in the SIGHAN 7 shared task; even comparing with the state-of-the-art systems, which used more resources, such as a considerable large dictionary, an idiom dictionary and other semantic information, our framework still obtains competitive results. Furthermore, to address the resource scarceness problem for training the SMT model, we generate around 2 million artificial training sentences using the Chinese character confusion sets, which include a set of Chinese characters with similar shapes and similar pronunciations, provided by the SIGHAN 7 shared task.

영작문 자동 채점 시스템을 위한 문맥 고려 단어 오류 검사기

ABSTRACT In this paper, we present a method that can detect context-sensitive word errors and generate correction candidates. Spelling error detection is one of the most widespread research topics, however, the approach proposed in this paper is adjusted for an automated English scoring system. A common strategy in context-sensitive word error detection is using a pre-defined confusion set to generate correction candidates. We automatically generate a confusion set in order to consider the characteristics of sentences written by second-language learners. We define a word error that cannot be detected by a conventional grammar checker because of part-of-speech ambiguity, and propose how to detect the error and generate correction candidates for this kind of error. An experiment is performed on the English writings composed by junior-high school students whose mother tongue is Korean. The f1 value of the proposed method is 70.48%, which shows that our method is promising comparing to the current-state-of-the art.Keywords:Context-sensitive Word Error, Error Detection/Correction, Confusion Set, English Automatic Scoring, Grammar-level Word Error

Speech-driven talking face using embedded confusable system for real time mobile multimedia

This paper presents a real-time speech-driven talking face system which provides low computational complexity and smoothly visual sense. A novel embedded confusable system is proposed to generate an efficient phoneme-viseme mapping table which is constructed by phoneme grouping using Houtgast similarity approach based on the results of viseme similarity estimation using histogram distance, according to the concept of viseme visually ambiguous. The generated mapping table can simplify the mapping problem and promote viseme classification accuracy. The implemented real time speech-driven talking face system includes: 1) speech signal processing, including SNR-aware speech enhancement for noise reduction and ICA-based feature set extractions for robust acoustic feature vectors; 2) recognition network processing, HMM and MCSVM are combined as a recognition network approach for phoneme recognition and viseme classification, which HMM is good at dealing with sequential inputs, while MCSVM shows superior performance in classifying with good generalization properties, especially for limited samples. The phoneme-viseme mapping table is used for MCSVM to classify the observation sequence of HMM results, which the viseme class is belong to; 3) visual processing, arranges lip shape image of visemes in time sequence, and presents more authenticity using a dynamic alpha blending with different alpha value settings. Presented by the experiments, the used speech signal processing with noise speech comparing with clean speech, could gain 1.1 % (16.7 % to 15.6 %) and 4.8 % (30.4 % to 35.2 %) accuracy rate improvements in PER and WER, respectively. For viseme classification, the error rate is decreased from 19.22 % to 9.37 %. Last, we simulated a GSM communication between mobile phone and PC for visual quality rating and speech driven feeling using mean opinion score. Therefore, our method reduces the number of visemes and lip shape images by confusable sets and enables real-time operation.