Text Representation Model Research Articles

A keyword extraction method from twitter messages represented as graphs

Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets.

텍스트 마이닝을 위한 그래프 기반 텍스트 표현 모델의 연구 동향

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

Product Quality and Safety Incident Information Tracking Based on Web News

In recent years, product quality and safety events happened frequently, which poses a threat to the health and property safety of consumers. With the development of the Internet, Web news has become one of the important channels for information release regarding product quality and safety issues. Therefore, identifying and tracking these incidents information plays an important role in providing early warning for product quality and safety problems. In this paper, through the analysis of the characteristics of Web news regarding product quality and safety incidents, an event-based multivector text representation model has been established, and a corresponding similarity calculation method has been developed. Based on K-Nearest Neighbor classification algorithm, a topic-tracking model has been set up, and finally the effectiveness of the model has been verified via the design of experiments.

Data Pre-processing Evaluation for Text Mining: Transaction/Sequence Model

Data pre-processing presents the most time consuming phase in the whole process of knowledge discovery. The complexity of data pre-processing depends on the data sources used. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in e-documents. We used the transaction/sequence model for text representation and sequence rule analysis as a method of modelling. We compare four datasets of different quality obtained from texts and pre-processed in different ways: data with identified the paragraph sequences, data with identified the sentence sequences, data with identified the paragraph sequences without stop words and data with identified the sentence sequences without stop words. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules. The results confirm some initial assumptions, but they also show that the stop words removal has a substantial impact on the quantity and quality of extracted rules in case of paragraph sequence identification. Contrary, in case of sentence sequence identification, removing the stop words has not any significant impact on the quantity and quality of extracted rules.

Towards graphical models for text processing

The rapid proliferation of the World Wide Web has increased the importance and prevalence of text as a medium for dissemination of information. A variety of text mining and management algorithms have been developed in recent years such as clustering, classification, indexing, and similarity search. Almost all these applications use the well-known vector-space model for text representation and analysis. While the vector-space model has proven itself to be an effective and efficient representation for mining purposes, it does not preserve information about the ordering of the words in the representation. In this paper, we will introduce the concept of distance graph representations of text data. Such representations preserve information about the relative ordering and distance between the words in the graphs and provide a much richer representation in terms of sentence structure of the underlying data. Recent advances in graph mining and hardware capabilities of modern computers enable us to process more complex representations of text. We will see that such an approach has clear advantages from a qualitative perspective. This approach enables knowledge discovery from text which is not possible with the use of a pure vector-space representation, because it loses much less information about the ordering of the underlying words. Furthermore, this representation does not require the development of new mining and management techniques. This is because the technique can also be converted into a structural version of the vector-space representation, which allows the use of all existing tools for text. In addition, existing techniques for graph and XML data can be directly leveraged with this new representation. Thus, a much wider spectrum of algorithms is available for processing this representation. We will apply this technique to a variety of mining and management applications and show its advantages and richness in exploring the structure of the underlying text documents.

A novel semi-supervised learning framework with simultaneous text representing

Text representation has received extensive attention in text mining tasks. There are various text representation models. Among them, vector space model is the most commonly used one. For vector space model, the core technique is term weighting. To date, a great deal of different term-weighting methods have been proposed, which can be divided into supervised group and unsupervised group. However, it is not advisable to use these two groups of methods directly in semi-supervised applications. In semi-supervised applications, the majority of the supervised term-weighting methods are not applicable as the label information is insufficient; meanwhile, the unsupervised term-weighting methods cannot make use of the provided category labels. Thus, a semi-supervised learning framework for iteratively revising the text representation by an EM-like strategy is proposed in this paper. Furthermore, a new supervised term-weighting method t f.sd f is proposed. T f.sd f has the ability to emphasize the importance of terms that are unevenly distributed among all the classes and weaken the importance of terms that are uniformly distributed. Experimental results on real text data show that the proposed semi-supervised learning framework with the aid of t f.sd f performs well. Also, t f.sd f is shown to be efficient for supervised learning.

Web Service Discovery by Integrating Structure and Reference Features of Description Documents

首先分析研究Web 服务描述文档(WSDL 文档)的两大特征——结构特征和参引特征,然后根据各个特征对Web 服务功能语义描述的影响,提出相应的Web 服务表示模型——多向量表示模型.区别于通用文本表示模型,该模型能够显式地表示Web 服务描述文档的本质特征.其中,结构特征语义表现在多向量空间的划分上,参引特征语义映射到子向量模型中特征权重的计算上.提出了基于多向量模型的Web 服务相似度计算方法,并实现了基于该模型的Web 服务发现原型系统.最后,在真实Web 服务描述文档集合上构造了一个具有不完全相关;This paper first investigates two main kinds of features of Web service description language (WSDL) documents: the structure features and the reference features. Next, a novel multi-vector model for Web services is introduced, which is distinguished from the general text representation model by the explicit features of Web services. The structure features are represented by multiple vector spaces and the term weighting in the sub-vector is determined by the reference features. A method to compute the similarity between two Web services is proposed and a Web service discovery prototype system based on this new model is implemented. Finally, a Web service discovery test collection is constructed, which has 1576 WSDL documents together with incomplete relevance judgments. The experimental results on this collection show that Web service discovery based on the proposed model is more effective than based on simple vector space model of text with the confidence of 95%.

A multi-layer text classification framework based on two-level representation model

Text categorization is one of the most common themes in data mining and machine learning fields. Unlike structured data, unstructured text data is more difficult to be analyzed because it contains complicated both syntactic and semantic information. In this paper, we propose a two-level representation model (2RM) to represent text data, one is for representing syntactic information and the other is for semantic information. Each document, in syntactic level, is represented as a term vector where the value of each component is the term frequency and inverse document frequency. The Wikipedia concepts related to terms in syntactic level are used to represent document in semantic level. Meanwhile, we designed a multi-layer classification framework (MLCLA) to make use of the semantic and syntactic information represented in 2RM model. The MLCLA framework contains three classifiers. Among them, two classifiers are applied on syntactic level and semantic level in parallel. The outputs of these two classifiers will be combined and input to the third classifier, so that the final results can be obtained. Experimental results on benchmark data sets (20Newsgroups, Reuters-21578 and Classic3) have shown that the proposed 2RM model plus MLCLA framework improves the text classification performance by comparing with the existing flat text representation models (Term-based VSM, Term Semantic Kernel Model, Concept-based VSM, Concept Semantic Kernel Model and Term + Concept VSM) plus existing classification methods.

Text Semantic Mining Model Based on the Algebra of Human Concept Learning

Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

GRAPH - BASED MODEL FOR TEXT REPRESENTATION

Text representation models are very important pre-processing step in various domains such as text mining, information retrieval, natural language processing. In this paper we summarize graph-based text representation models. Graph-based model can capture structural information such as the location, order and proximity of term occurrence, which is discarded under the standard text vector representation models. We have tested this graph model in Vietnamese text classification system.

Fontes intelligentes, textèmes et typographie dynamique

The digital text representation model used by the industry today is the model proposed by the Unicode standard. This model, based on the concepts of character and glyph, provides the foundations for the so-called smart font technologies such as AAT, OpenType and Graphite. Using a formal description of Unicode's text model, the authors compare the three said font formats, especially regarding paragraph breaking and how mapping information is preserved between characters and glyphs, in other words, between content and presentation. In the second part of the paper, the authors propose a novel approach to these two problems through an alternative text model based on the texteme concept and dynamic typography. MOTS-CLES : Unicode, fontes intelligentes, modele de texte, caractere, glyphe, paragraphage, texteme, typographie dynamique, OpenType, AAT, Graphite

An investigation of content representation using text grammars

We extend prior work on a model for natural language text representation and retrieval using a linguistic device called text grammar. We demonstrate the value of this approach in accessing relevant items from a collection of empirical abstracts in a medical domain. The advantage, when compared to traditional keyword retrieval, is that this approach is a significant move towards knowledge representation and retrieval. Text representation in this model includes keywords and their conceptual roles in the text. In particular, it involves extracting TOPIC predicates representing the research issue addressed and DESIGN predicates representing important methodological features of the empirical study. Preliminary experimentation shows that keywords exhibit a variety of text-grammar roles in a text database. Second, as intuitively expected, retrieval using TOPIC predicates identifies a smaller subset of texts than Boolean retrieval does. These empirical results along with the theoretical work indicate that the representation and retrieval strategies proposed have a significant potential. Finally, EMPIRICIST, a prototype system is described. In it the text representation predicates are implemented as a network while retrieval is through constrained-spreading activation strategies.