Text Similarity Measures Research Articles

AUTOMATIC ESSAY GRADING SYSTEM FOR SHORT ANSWERS IN ENGLISH LANGUAGE

Automatic Essay Grading (AEG) system is defined as the computer technology that evaluates and grades written prose. The short essay answer, where the es say is written in short sentences where it has two types the open ended short answer and the close ended sho rt answer where it is our research domain based on the computer subject. The Marking of short essay answers automatically is one of the most complicated domains because it is relying heavily on the semant ic similarity in meaning refers to the degree to wh ich two sentences are similar in the meaning where both used similar words in the meaning, in this case Humans are able to easily judge if a concepts are r elated to each other, there for is a problem when S tudent use a synonym words during the answer in case they forget the target answer and they use their alterna tive words in the answer which will be different from th e Model answer that prepared by the structure. The Standard text similarity measures perform poorly on such tasks. Short answer only provides a limited content, because the length of the text is typicall y short, ranging from a single word to a dozen word s. This research has two propose; the first propose is Alternative Sentence Generator Method in order to generate the alternative model answer by connecting the method with the synonym dictionary. The second proposed three algorithms combined together in matching phase, Commons Words (COW), Longest Common Subsequence (LCS) and Semantic Distance (SD), these algorithms have been successfully used in many Natural Language Processi ng systems and have yielded efficient results. The system was manually tested on 40 questions answered by three students and evaluated by teacher in class. The proposed system has yielded %82 corre lation-style with human grading, which has made the system significantly better than the other stat e of the art systems.

A Survey of Text Similarity Approaches

ABSTRACT Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities. Furthermore, samples of combination between these similarities are presented. General Terms Text Mining, Natural Language Processing. Keywords BasedText Similarity, Semantic Similarity, String-Based Similarity, Corpus-Based Similarity, Knowledge-Based Similarity. NeedlemanWunsch 1. INTRODUCTION Text similarity measures play an increasingly important role in text related research and applications in tasks Nsuch as information retrieval, text classification, document clustering, topic detection, topic tracking, questions generation, question answering, essay scoring, short answer scoring, machine translation, text summarization and others. Finding similarity between words is a fundamental part of text similarity which is then used as a primary stage for sentence, paragraph and document similarities. Words can be similar in two ways lexically and semantically. Words are similar lexically if they have a similar character sequence. Words are similar semantically if they have the same thing, are opposite of each other, used in the same way, used in the same context and one is a type of another. DistanceLexical similarity is introduced in this survey though different String-Based algorithms, Semantic similarity is introduced through Corpus-Based and Knowledge-Based algorithms. String-Based measures operate on string sequences and character composition. A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison. Corpus-Based similarity is a semantic similarity measure that determines the similarity between words according to information gained from large corpora. Knowledge-Based similarity is a semantic similarity measure that determines the degree of similarity between words using information derived from semantic networks. The most popular for each type will be presented briefly. This paper is organized as follows: Section two presents String-Based algorithms by partitioning them into two types character-based and term-based measures. Sections three and four introduce Corpus-Based and knowledge-Based algorithms respectively. Samples of combinations between similarity algorithms are introduced in section five and finally section six presents conclusion of the survey.

Semantic similarity measurement using historical google search patterns

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

An overview of textual semantic similarity measures based on web intelligence

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields. The problem is that traditional approaches to semantic similarity measurement are not suitable for all situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this problem. These techniques use some kinds of web intelligence to determine the degree of similarity between text expressions. These techniques implement a variety of paradigms including the study of co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is to substitute the traditional techniques where necessary.

A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features

Measuring relatedness of two papers is an issue which arises in many applications, e.g., recommendation, clustering and classification of papers. In this paper, a digital library is modeled as a directed graph; each node representing three different types of entities: papers, authors, and venues, and each edge representing relationships between these entities. Based on this graph model, six different types of relations are considered between two papers, and a new metric is proposed for evaluating relatedness of the papers. This metric only focuses on the relational features, and does not consider textual features. We have used it in combination with a textual similarity measure in the context of citation recommendation systems. Experimental results show that using this metric can successfully improve the quality of the recommendations.

Electrical evoked potentials prediction model in visual prostheses based on support vector regression with multiple weights

Electrical evoked potentials (EEPs) elicited by electrical stimuli to the optical nerve are an important study object in optical nerve visual prostheses to investigate the temporal property of responses of the visual cortex. Concentrating on reducing the cost of the visual prostheses research, this paper proposes an intelligent EEPs prediction model based on the support vector regression with multiple weights (SVR–MW) method in substitution of numerous biological experiments. In SVR–MW, to improve the predictive performance of traditional SVR, more temporal weights and similarity-based weights are given to the recent training data extracted from similar experimental cases for new electrical stimulus parameters than the distant data from less similar cases during regression estimation. For temporal weight (TW), we propose two TW functions i.e., linear temporal weight (LTW) function and exponential temporal weight (ETW) function to calculate the temporal weight of training sample at different time nodes. For similarity-based weight (SW), the similarity measurement (SM) is the key issue, and we adopt the multi-algorithm-oriented hybrid SM methods i.e., textual SM, numerical SM, interval SM and fuzzy SM to solve the SW computation for training data derived from different experimental cases. The proposed method was empirically tested with data collected from actual EEPs eliciting experiments. Empirical comparison shows that SVR–MW is feasible and validated for EEPs prediction in visual prostheses research.

Text Clustering Using a Suffix Tree Similarity Measure

In text mining area, popular methods use the bag-of-words models, which represent a document as a vector. These methods ignored the word sequence information, and the good clustering result limited to some special domains. This paper proposes a new similarity measure based on suffix tree model of text documents. It analyzes the word sequence information, and then computes the similarity between the text documents of corpus by applying a suffix tree similarity that combines with TF-IDF weighting method. Experimental results on standard document benchmark corpus RUTERS and BBC indicate that the new text similarity measure is effective. Comparing with the results of the other two frequent word sequence based methods, our proposed method achieves an improvement of about 15% on the average of F-Measure score.

Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

Electrical evoked potentials (EEPs) time series prediction is a novel topic concentrating on reducing the cost of the visual prostheses research. Support vector machine (SVM), a superior neural network algorithm, is a powerful tool for time series forecasting but is insensitive to multivariate analysis. Meanwhile, similarity measurement (SM), a key technology in case-based reasoning, has been applied in a wide variety of fields but is only limited to the point-to-point computation. This paper firstly attempts to take the advantages of SM and SVM to generate a high performance EEPs predictor. Four independent SM metrics, i.e. fuzzy SM, numeric SM, textual SM and interval SM are employed to calculate the similarities between input variables (including electrical stimulation parameter and spatial parameter) and corresponding experimental values. Then SVM is utilized to predict EEPs behavior in terms of the temporal input. Furthermore, we add the similarities and temporal weights into SVM to indicate that recent data from similar experimental cases could provide more information than distant data from dissimilar ones. Due to the dynamic property, the new SVM is called dynamic SVM, i.e. DSVM and the predictor is named SM-DSVM. How to implement the hybrid predictor with grid-search for parameter optimization is illustrated in detail. In the empirical comparison, the predictive performances on 30 hold-out data are used to make comparisons between SM-DSVM and other comparative predictors. Empirical results show that SM-DSVM is feasible and validated for EEPs prediction in visual prostheses research.

Crossing textual and visual content in different application scenarios

This paper deals with multimedia information access. We propose two new approaches for hybrid text-image information processing that can be straightforwardly generalized to the more general multimodal scenario. Both approaches fall in the trans-media pseudo-relevance feedback category. Our first method proposes using a mixture model of the aggregate components, considering them as a single relevance concept. In our second approach, we define trans-media similarities as an aggregation of monomodal similarities between the elements of the aggregate and the new multimodal object. We also introduce the monomodal similarity measures for text and images that serve as basic components for both proposed trans-media similarities. We show how one can frame a large variety of problem in order to address them with the proposed techniques: image annotation or captioning, text illustration and multimedia retrieval and clustering. Finally, we present how these methods can be integrated in two applications: a travel blog assistant system and a tool for browsing the Wikipedia taking into account the multimedia nature of its content.

Multishape-features and text-feature integration on 3D model similarity retrieval

In this paper, we describe several 3D shape descriptors and integrate a textual descriptor with them for 3D model retrieval. We analyse five Shape-Feature Vector (FV) integration approaches, namely Pure FV Integration (PFI), Reduced FV Integration (RFI), Weight-Associated RFI (WRFI), Distance Integration (DI) and Rank Integration (RI). By running all possible-combinations of weighting factors on a training data set, the best weighting factor for each approach is obtained. Our experiments show that the best weighting factors improve the retrieval performance on not only the training data set, but also other data sets. This paper also shows that Distance Integration delivers the best retrieval effectiveness and Reduced FV Integration has the capability to deal with unknown query. In addition, the Distance Integration provides faster processing as it uses precomputed pair-wise distance and is more advantageous than PFI because of the dimension reduction. Hence, the use of both approaches (DI and RFI) is proposed. This paper also explains a use of the model file name as the only resource for text feature extraction. We study several textual similarity measures and then integrate multishape and text features into 3D model retrieval. Our experiments show that text feature can discriminate 3D models to each other in a certain degree of effectiveness, and integration text feature with multishape-feature improves retrieval effectiveness.

Overview and semantic issues of text mining

Text mining refers to the discovery of previously unknown knowledge that can be found in text collections. In recent years, the text mining field has received great attention due to the abundance of textual data. A researcher in this area is requested to cope with issues originating from the natural language particularities. This survey discusses such semantic issues along with the approaches and methodologies proposed in the existing literature. It covers syntactic matters, tokenization concerns and it focuses on the different text representation techniques, categorisation tasks and similarity measures suggested.

Efficient plagiarism detection for large code repositories

AbstractUnauthorized re‐use of code by students is a widespread problem in academic institutions, and raises liability issues for industry. Manual plagiarism detection is time‐consuming, and current effective plagiarism detection approaches cannot be easily scaled to very large code repositories. While there are practical text‐based plagiarism detection systems capable of working with large collections, this is not the case for code‐based plagiarism detection. In this paper, we propose techniques for detecting plagiarism in program code using text similarity measures and local alignment. Through detailed empirical evaluation on small and large collections of programs, we show that our approach is highly scalable while maintaining similar levels of effectiveness to that of the popular JPlag and MOSS systems. Copyright © 2006 John Wiley & Sons, Ltd.

A fast document copy detection model

Text similarity measure is a common issue in Information Retrieval, Text Mining, Web Mining, Text Classification/Clustering and Document Copy Detection etc. The most popular approach is word frequency based scheme, which uses a word frequency vector to represent a document. Cosine function, dot product and proportion function are regular similarity measures of vector. But they are symmetric similarity measures, which cannot find out the subset copies. In this paper we present the concepts of asymmetric similarity model and heavy frequency vector (HFV). The former can detect subset copies well, and the latter can save a great resources and CPU time. We have developed two new asymmetric measures: heavy frequency vector (HFM) and Heavy inclusion proportion model HIPM. The HFM and HIPM are derived from cosine function and proportion function by combining asymmetric similarity concept with HFV. The HFV is to truncate the original full frequency vector to a short vector. We can adjust the parameter of HFV to balance the model’s performance. The paper illustrates the aspects of asymmetric similarity and HFV models by several experiments.

Adaptive name matching in information integration

Identifying approximately duplicate database records that refer to the same entity is essential for information integration. The authors compare and describe methods for combining and learning textual similarity measures for name matching.

Automatic text decomposition and structuring

Sophisticated text similarity measurements are used to determine relationships between natural-language texts and text excerpts. The resulting linked hypertext maps can be decomposed into text segments and text themes, and these decompositions are usable to identify different text types and text structures, leading to improved text access and utilization. Examples of text decomposition are given for expository and non-expository texts.