Fusion News Elements of News Text Similarity Calculation

Word2vec word vector algorithm and Doc2vec text vectoring algorithm calculate the similarity between words and between texts from the perspective of vector space without considering the semantic similarity between texts. Thus, in this paper, an automatic evaluation algorithm is proposed to combine semantic similarity based on synonym lexical forest and text similarity based on vector space. Meanwhile, Sentiment analysis was used to obtain the emotional score of the reference answers and students' answers in the subjective questions. Finally, the final score of students' answers was obtained by combining text similarity and emotion scores. In this paper, logistics professional education is the experimental object of the experiment. The experimental results show that the algorithm has achieved good scoring results.

The relevance of the article is determined by the attention of modern linguists to the study of texts of various genres and their impact on the target audience. The object of the research is news texts, which resort to convincing the addressee of the truthfulness of the events described, influencing their perception, judgment and worldview. There arises a problem of how news texts guide their readers in forming a particular view on the event. The persuasive nature of news texts has made it possible to study them with the help of rhetorical modes of persuasion: ethos, logos, and pathos. The purpose of the research is to establish models of structuring news texts at the level of their composition and linguistic ornamentation. The novelty of the work consists in the application of the constructionist approach, based on morphosyntactic constructions as pairings of form, meaning and functions, stored at the mental level. Constructs as speech realization of constructions represent various relations of the surrounding world. During the research, the method of continuous sampling, component, contextual and rhetorical analyses were applied. The research material comprises 150 news texts on the English website BBC News. The results of the study. Models of structuring news texts reflect their impact on the target audience. Depending on the type of construct and its placement in the headline, lead, post-lead and the main text of the news, three models are distinguished: descriptive, authorizing and emotive. According to the descriptive model, news texts appeal to logos via arguments, judgments, and facts and are characterized by focusing, that is, the repetition of identical or synonymous agentive constructs in successive paragraphs. The authorizing model of news texts structuring is based on ethos, that is, appeal to famous personalities, experts and witnesses of the event as a mode of persuasion and a source of information. This model is embodied by predicative constructs indicating speech process in the headline or lead. The emotive model of arranging news texts appeals to pathos and is represented by emotive predicative constructs or evaluative pairings. Further research foresees considering news reports about the speeches of famous personalities and politicians and narratives using the constructionist approach.

Plagiarism in e-homework becomes increasingly severe. Based on the study of the research on e-homework plagiarism detection system and the key issues on e-homework plagiarism, an e-homework anti-plagiarism system is designed and implemented. Similarity of text, which is the plagiarism standard of an e-homework, is calculated based on TF-IDF and Vector Space Model (VSM) theory. It can detect the checking e-homework copied from one or more other e-homework, thus prevent most of the plagiarism. Most of the time, e-homework are uploaded in the last few minutes of the class, and Microsoft Message Queue is used to relieve the stress of the system by setting up an E-homework Upload Queue. Time preference tragedy is used, and it greatly reduces the plagiarism.

Vector Space Models (VSMs) represent documents as points in a vector space derived from term frequencies in the corpus. This level of abstraction provides a flexible way to represent complex semantic concepts through vectors, matrices, and higher-order tensors. In this paper we utilize a number of VSMs on a corpus of judicial decisions in order to classify cases in terms of legal factors, stereotypical fact patterns that tend to strengthen or weaken a side's argument in a legal claim. We apply different VSMs to a corpus of trade secret misappropriation cases and compare their classification results. The experiment shows that simple binary VSMs work better than previously reported techniques but that more complex VSMs including dimensionality reduction techniques do not improve performance.

This paper presents ARGOsearch, an information retrieval system which improves the search quality in library management systems. We have proposed a general and extensible architecture for ARGOsearch based on relevance criteria that sorts the user query results considering text similarity and context information such as statistical data and the user profile. We have experimentally evaluated our approach using the vector space model as baseline.

This study presents an overview of the research aimed at optimizing library information retrieval through the utilization of the Vector Space Model (VSM) method in a computer science context. Libraries, as publicly financed collections, provide extensive knowledge resources, eliminating the need for individual book purchases. However, the challenge lies in efficiently navigating the expanding library collections. To tackle this issue, the study employs information retrieval techniques, particularly the VSM method, which assesses term similarity by assigning weights to terms, enabling document and query representation as vectors. The relevance between documents and queries is measured through vector similarity. This approach, integrated with indexing, streamlines collection retrieval in libraries. Employing the Waterfall model for system development, the research outlines phases like analysis, design, coding, testing, and implementation. While effective, the model's rigidity in accommodating evolving requirements poses limitations. The VSM method's numerical representation of text documents facilitates precise similarity calculations, supported by TF-IDF values indicating term importance in documents relative to the corpus. The study further extends to system design using UML diagrams and a visitor interface, integrating VSM for efficient search functionality. Black-box testing confirms the robustness of the system components and interfaces. Overall, this research presents a systematic approach to enhance information retrieval in libraries, emphasizing the VSM's pivotal role in optimizing document searches within expansive collections.

This paper proposes and method to improve retrieval performance of the vector space model (VSM) by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, incorporated into the retrieval model, which is built by using a sequence of linear transformations, is information such as inter-document similarity values. Then, the high-dimensional and sparse vectors are reduced by SVD (Singular Value Decomposition) and transformed into the low-dimensional vector space, namely the space representing the latent semantic meanings of the words. The method was experimented on through two test collections, Medline collection and Cranfield collection. Improvement of average precision compared with LSI (Latent Semantic Indexing) model were 4.03% (Medline) and 24.87% (Cranfield) for the two training data sets, and 0.01% (Medline) and 4.89% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve the user-supplied relevance information for a long term in the system and to use the information later.

Document categorization is the process of classifying documents from many mixed documents automatically, and the main problem is how to express document content in vector space completely. This paper proposes a new model named Latent Semantic Analysis (LSA) + word2vec to categorize documents. This is the first attempt of combining word2vec with LSA at document categorization and it can map document to vector space under the premise of keeping document contents fully. At first, we create a term by document matrix and the element of which is decided by Term Frequency-Inverse Document Frequency (TF-IDF) weighting and word vector trained by word2vec. This matrix is a 3-dimensional matrix and it can describe the meaning of every word and the content of every document exactly. Secondly, Singular Value Decomposition (SVD) is executed on the matrix and lower computational complexity is gained from this. The model is named LSA + word2vec. Then, document vector gained from the new model are put into Convolutional Neural Network (CNN) to train. CNN is an efficient deep learning algorithm, which improves the accuracy of classification greatly. We evaluate the performance based on the 20newsgroups corpus. The results show that our new model achieves better effects on document categorization tasks, and the accuracy made about 15% improvement than traditional methods, such as LSA and Vector Space Model (VSM).

Chapter 1 - Why Geometric Algebra?

Huge digital data is typical nowadays. Consequently, text clustering has become a crucial text mining technique nowadays. Organizing search information into groups (or clusters) not only makes the search results more meaningful, but makes the system more efficient since the user views only the relevant information and ignores the rest. An example is clustering web search engine result into meaningful results. Many text clustering approaches and their corresponding algorithms exist, but none has been found to be sufficient. There is also insufficient understanding of the algorithms as well as lack of agreed formal classification of the algorithms. There is thus, need for an in-depth study of the various algorithms. In this paper, we describe the Vector Space Model (VSM) method of text representation. We also give an overview of the text clustering approaches that apply the VSM .These include distance based approach, feature extraction approach, density-based approach, grid-based approach, and neural networks approach. We describe the characteristics of each accompanied by a representative algorithm. The paper thus informs researchers of text mining concerning the current state of affairs of text clustering algorithm. Keywords: clusters, text clustering, text mining, vector space model.

Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning

Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in‐depth and comprehensive study of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word‐shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG‐SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm.

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under quantlet. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

Currently, the data that dealt by traditional text clustering methods is small. Text representation model that text clustering used is traditional vector space model (VSM). The traditional text clustering has defect of low efficiency when processing big data. The quality is bad when using traditional VSM model for text representation. To solve these two problems, this paper puts forward a VSM model for text representation, which is improved by Term Frequency(TF), and using Mahout to text clustering. Experimental results show that use Mahout for text clustering has a higher efficiency than traditional text clustering; this novel VSM model can improve the text clustering quality.

In law, a judgment is a decision by a court that resolves a controversy and determines the rights and liabilities of parties in a legal action or proceeding. In 2013, China Judgments Online system was launched officially for record keeping and notification, up to now, over 23 million electronic judgment documents are recorded. The huge amount of judgment documents has witnessed the improvement of judicial justice and openness. Document categorization becomes increasingly important for judgments indexing and further analysis. However, it is almost impossible to categorize them manually due to their large volume and rapid growth. In this paper, we propose a machine learning approach to automatically classify Chinese judgment documents using machine learning algorithms including Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM). A judgment document is represented as vector space model (VSM) using TF-IDF after words segmentation. To improve performance, we construct a set of judicial stop words. Besides, as TF-IDF generates a high dimensional feature vector, which leads to an extremely high time complexity, we utilize three dimensional reduction methods. Based on 6735 pieces of judgment documents, extensive experiments demonstrate the effectiveness and high classification performance of our proposed method.