Features Of Sentences Research Articles

Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks

In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.

Sentiment Analysis of Weibo Comments Based on Graph Neural Network

Weibo is one of the most important online social platforms. Currently, user comments are increasing rapidly, which makes data management difficult. Comments show the non-standardized and colloquial form of expression. Traditional sentiment analysis techniques are no longer applicable to unspecified sentence analysis tasks. To mitigate overreliance on text sequences, ignoring syntactic structure, and the poor interpretability of feature space that are typical of traditional classification models, a sentiment classification model based on a graph neural network (GNN) is developed in this study. For each comment text, the dependency syntax is used to construct the semantic graph of the short text. Aiming at the heterogeneity of the semantic graph, the spatial domain graph filter is designed for feature extraction. Concurrently, long short-term memory (LSTM) is used as a state updater to filter node noise. In this method, a graph neural network is used as a semantic parser to encode the syntactic dependency tree, which can extract the semantic and syntactic features of sentences concurrently. Experimental results show that GNN-LSTM has achieved superior performance in the Weibo comments dataset by achieving 95.25% accuracy and 95.22% F1 score.

Text sentiment classification method based on DPCNN and BiLSTM

In recent years, deep learning network models have been widely used in the aspect of text emotion classification and have achieved remarkable achievements. The traditional TextCNN network can only extract local spatial features of sentences, while the improved DPCNN model has the ability to capture long-distance dependence of the text by deepening the network depth. At the same time, bi-LSTM model is characterized by learning temporal information of text. Therefore, this paper combines the two models, which can not only obtain the spatial local information of the text, but also further strengthen the ability to understand and learn the semantic association information of the text. Experimental results show that the classification effect of the model used in this paper is better than the single model.

TypeFormer: Multiscale Transformer With Type Controller for Remote Sensing Image Caption

Image captioning in remote sensing can help us understandthe inner attributes of the objects and the outer relations between different objects. However, the existing image captioning algorithms lack the ability of global representation, and cannot obtain object relations over long distances. In addition, these algorithmics generate captions randomly without consideration of the specific demands. To this end, we propose a pure transformer architecture with caption type controller for remote sensing image captioning. Specifically, a multi-scale vision transformer is adopted for the image representation, where the global and detailed content can be captured with multi-head self-attention layers. A transformer decoder is then introduced to successively translate the image features into comprehensive sentences. The optional block called the caption type controller is designed to consider the types of captions through caption type matrix sets according to the demands, embedding the learnable sentence feature with the required type. The comparison and ablation experiments conducted on the Remote Sensing Image Captioning Dataset (RSICD) dataset demonstrate that the proposed framework outperforms the current state-of-the-art image captioning methods. The experiments conducted on the FloodNet caption dataset further illustrate that the proposed methods can effectively generate specific types of captions.

Machine identification of potential manufacturing process failure modes based on process constituent elements

It is urgent to solve the problems of low efficiency, high cost and lack of system integrity when PFMEA identifies potential process failure modes in the process of multi-variety and small batch production. Current study innovatively proposes a machine identification method of potential process failure modes based on the combination of process constituent elements (PCE) model and feature case-basedreasoning represented by extension. Firstly, the specific description of the process content is developed with the PCE and the way of expression is standardized through the method of data mining. Then, the PCE of the normative knowledge representation of extension feature cases were constructed, and natural language processing (NLP) technology is employed to solve the feature similarity between the same PCE after the specification of the sentence and chunk features respectively, and case-based reasoning and extension operator are applied to carry out analogical reasoning and calculation for the feature cases with high similarity degree of feature attributes, so as to realize the machine identification of the potential process failure modes in the manufacturing process. Finally, the proposed method is specifically applied to the three parts assembly process of an aircraft assembly to verify the effectiveness and applicability of the proposed method.

Cross-Modal Hybrid Feature Fusion for Image-Sentence Matching

Image-sentence matching is a challenging task in the field of language and vision, which aims at measuring the similarities between images and sentence descriptions. Most existing methods independently map the global features of images and sentences into a common space to calculate the image-sentence similarity. However, the image-sentence similarity obtained by these methods may be coarse as (1) an intermediate common space is introduced to implicitly match the heterogeneous features of images and sentences in a global level, and (2) only the inter-modality relations of images and sentences are captured while the intra-modality relations are ignored. To overcome the limitations, we propose a novel Cross-Modal Hybrid Feature Fusion (CMHF) framework for directly learning the image-sentence similarity by fusing multimodal features with inter- and intra-modality relations incorporated. It can robustly capture the high-level interactions between visual regions in images and words in sentences, where flexible attention mechanisms are utilized to generate effective attention flows within and across the modalities of images and sentences. A structured objective with ranking loss constraint is formed in CMHF to learn the image-sentence similarity based on the fused fine-grained features of different modalities bypassing the usage of intermediate common space. Extensive experiments and comprehensive analysis performed on two widely used datasets—Microsoft COCO and Flickr30K—show the effectiveness of the hybrid feature fusion framework in CMHF, in which the state-of-the-art matching performance is achieved by our proposed CMHF method.

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

Off-Topic Detection of Business English Essay Based on Deep Learning Model

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.

Роль вопросительных предложений в поэтическом тексте

The aim of the study is to identify the features of interrogative sentences functioning in a poetic text. The article highlights one of the most important aspects of linguistic stylistics: the relationship between the types of syntactic constructions and the sphere of their aesthetically significant functioning. The scientific originality of the research lies in the very approach to solving the developed topic as a complex one: the context conditions, syntactic features of linguistic units, facts of composition, emotional and stylistic features of the verse are taken into account when identifying functional and semantic types of interrogative sentences. As a result of the research, the functional and semantic nature of the issue is determined, considering the emotionally stylistic characteristics of the context. The dynamic character of the question type belonging to a certain functional-semantic group is illustrated; the role of the question in the actualization of the poetic text is established, and it is proved that the communicative orientation of interrogative sentences is preserved in the poetic text.

BIBC: A Chinese Named Entity Recognition Model for Diabetes Research

In the medical field, extracting medical entities from text by Named Entity Recognition (NER) has become one of the research hotspots. This thesis takes the chapter-level diabetes literature as the research object and uses a deep learning method to extract medical entities in the literature. Based on the deep and bidirectional transformer network structure, the pre-training language model BERT model can solve the problem of polysemous word representation, and supplement the features by large-scale unlabeled data, combined with BiLSTM-CRF model extracts of the long-distance features of sentences. On this basis, in view of the problem that the model cannot focus on the local information of the sentence, resulting in insufficient feature extraction, and considering the characteristics of Chinese data mainly in words, this thesis proposes a Named Entity Recognition method based on BIBC. This method combines Iterated Dilated CNN to enable the model to take into account global and local features at the same time, and uses the BERT-WWM model based on whole word masking to further extract semantic information from Chinese data. In the experiment of diabetic entity recognition in Ruijin Hospital, the accuracy rate, recall rate, and F1 score are improved to 79.58%, 80.21%, and 79.89%, which are better than the evaluation indexes of existing studies. It indicates that the method can extract the semantic information of diabetic text more accurately and obtain good entity recognition results, which can meet the requirements of practical applications.

Extractive Text Summerization Pada Berita Berbahasa Indonesia Menggunakan Algoritma Support Vector Machine

According to the Program for International Student Assessment (PISA) for the 2018 survey of 61 countries that participated in PISA, the reading interest of the Indonesian people still received a low score of 358 out of an overall average score of 472. One of the consequences of low reading is the difficulty of understanding the content of reading, especially for long and many texts, so it will be easier to read the summary. With advances in text summarization technology can be done using text mining methods. text mining will retrieve information on big data from text-based documents, the summary process will take the main points of news or important sentences without changing the content of the reading or also called extraction techniques. To get maximum results, the weighting is done by extracting sentence features based on numerical data, quotations, sentence length, sentence position in paragraphs, and overall sentence position. The research methodology uses knowledge discovery in database (KDD) and modeling using support vector machine algorithms. Testing or evaluation using recall, precision and F-measure. The best research result is the scenario of comparison of test data and training data 7:3, using the Linear kernel, with accuracy 72,4%, precision 63,4%, recall 51,9%, and F-measure 57,1%.

Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

Computer Music Query by Humming Considering Subsequence Matching Algorithm

Facing massive music data, how to create its index in content-based retrieval to improve retrieval speed is a crucial research content. In the feature extraction from music content based on sentence and index creation, the subsequence matching algorithm of music melody is stored in the database. Database However, when users conduct query by humming (QBH), they may hum a piece of music containing multiple sentences for retrieval, which requires the generation of multiple sentence features. Hence, a solution to single sentence feature, multi-sentence conversion and matching is proposed and applied to the retrieval system, with an excellent retrieval effect. The relevant achievements are also applicable to the retrieval system of time series data with a similar structure.

Target-oriented Opinion Words Extraction via graph convolutional network

Target-oriented opinion word extraction (TOWE) is a sequence labelling subtask of aspect-level sentiment analysis (ABSA), which aims to extract corresponding opinion words for a given sentence and opinion target. In view of the existing related work, the structural information of sentences is not fully considered. In this study, we explore the integration of neural network and dependency tree to handle the TOWE and propose a model based on graph convolution network (GCN). The model uses the long short-term memory network (LSTM) to learn the semantic features of sentences. On this basis, the model uses the GCN to model the sentence structure through dependency tree and capture the syntactic dependency relationship between the target word and the opinion word. The experimental results show that GCN can effectively improve the performance of TOWE. The F1 values of the model on the semeval-rest14 and semeval-laptop14 reached 82.60% and 74.45%.

AXIOLOGICAL FEATURES OF CONDITIONAL SENTENCES IN ENGLISH

This article deal with the analysis of the axiological potential of conditional sentences in the English language. The aim of the work is to reveal axiological features of conditional sentences in English in terms of expressing values in a communicative setting. In order to achieve the aim of the study, the following tasks are set: review of related literature; analysis of axiological characteristics of English conditional sentences; identification and classification of values expressed by conditional sentences in English. The results of research show that conditional sentences in English can: a) contribute to the verbalization of axiological picture of the world; b) verbalize some basic human values as power, benevolence, security, achievement, hedonism, self-direction, stimulation, conformity, universalism in a specific communicative context; c) express values with the help of whole syntactic meaning.

Deep neural network for text anomaly detection in SIoT

Unsupervised textual anomaly detection, which discovers anomalies from unlabeled texts, is critical to improve the cybersecurity and interaction ability among the objects in the Social Internet of Things (SIoT). Recently, detecting anomalies by deep neural networks has become a popular trend. Specially, context vector data description (CVDD) method shows the promising performance. However, CVDD has two limitations: (1) it uses an one-class classification objective to constrain the sentence embeddings, which leads the learned embeddings to lose content information of text. (2) Scalar-based attention weights, which are used to extract sentence features, fail to focus on dimensional properties in a word. Learning the text contents and the dimensional properties is important for detection task in SIoT, which can help detector capture the difference between normal and anomaly texts. To overcome these limits, this paper proposes a textual anomaly detection network. First, an adversarial training strategy is designed to fight against the problem of missing content information. Second, a textual anomaly detection module with multiple dimensional transformation matrices is constructed to learn dimensional properties of words in diverse semantic subspaces. Experimental results on several textual datasets show that our proposed method outperforms CVDD and other strong baselines.

Neutrosophic Logic-Based Document Summarization

Nowadays, rich quantity of information is offered on the Net which makes it hard for the clients to detect necessary information. Programmed techniques are desirable to effectively filter and search useful data from the Net. The purpose of purported text summarization is to get satisfied content handling with information variety. The main factor of document summarization is to extract benefit feature. In this paper, we extract word feature in three group called important words. Also, we extract sentence feature depending on the extracted words. With increasing knowledge on the Internet, it turns out to be an extremely time-consuming, exhausting, and boring mission to read the whole content and papers and get the relevant information on precise topics

Comparison of Data Augmentation Methods in Pointer—Generator Model

In this research, we proposed a data augmentation method using topic model for Pointer—Generator model. This method is that adding important sentences to an article as extended article. Furthermore, we compare our proposed method with data augmentation methods using Easy Data Augmentation (EDA), LexRank and Luhn. EDA consists of synonym replacement, random insertion, random swap, and random deletion. LexRank is based on Google’s search method and Luhn defines sentence features and ranks sentences. We considered which method is suitable for data augmentation. We confirm that most accurate model is the model using data augmentation method by topic model.

Single-shot Semantic Matching Network for Moment Localization in Videos

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.

Структура вопросительного предложения с именным подлежащим во французском языке в диахронном аспекте

The article is focused on the study of interrogative sentences with a nominal subject in the French language in terms of structural criteria in a diachronic aspect. The purpose of this study is to reveal the features of the so-called complex inversion in interrogative sentences. Special attention is paid to terminological problems. Pronominal reprise is recognized by the authors as the most correct term for an interrogative construction with a nominal subject duplicated by a personal pronoun. The lack of uniformity in terminology indicates a multidimensional nature of the inversion phenomenon in the French language. For instance, in addition to interrogative sentences, it can be used in other contexts: in simple declarative sentences after certain adverbs, in subordinate clauses after some conjunctions, in exclamatory sentences, etc. The authors highlight and describe the characteristic features of interrogative sentences with a nominal subject in terms of diachrony. Pronominal reprise appears in interrogative constructions as early as in the 16th century, mainly in general questions, subsequently extending to special questions. In the 16th – 17th centuries, the subject did not regularly precede the verb; the same applied to interrogative words, which could be placed both to the left and to the right of the subject. In sentences with a nominal subject, reprise was not the only possible structural option. The following were also used: direct word order, construction question word + est-ce que, and simple inversion. The authors come to the conclusion that pronominal reprise had finally replaced simple inversion of the nominal subject in general questions in French by the end of the 17th century.