The use of anaphoric resolution for document description in information retrieval

A novel term weighting scheme for text classification: TF-MONO

The performance of text classification can be affected by the choice of appropriate term weighting scheme as well as other parameters. The terminology supervised term weighting scheme has become popular in recent years, as it may provide discriminative representation in vector space for text documents belonging to different classes. A term weighting scheme generally consists of three factors, namely term frequency factor, collection frequency factor, and length normalization factor. The researchers mostly have been focused on developing new collection frequency factors in term weighting studies. However, the term frequency factor has an important role, especially in supervised term weighting. In this study, we extensively analyzed the effects of using different term frequency factors on seven supervised term weighting schemes. While six of these supervised term weighting schemes were applied in the previous studies in the literature, we derived one of them from an existing feature selection method and it was not used as a weighting method before. This analysis is performed using SVM and Roccio classifiers on two widely known benchmark datasets with different characteristics. Experimental results showed that modification of term frequency factor in supervised term weighting schemes increased the performance of almost all weighting schemes. Also, term weighting schemes using square root function-based term frequency factor (SQRT_TF) are more successful than the ones using term frequency (TF) and logarithmic function-based term frequency (LOG_TF) factors. TF term frequency factor seems as the least effective one among three different term frequency factors according to the experimental results and statistical analysis.

Term weighting is an important task in many applications, such as information retrieval, extraction of significant words or automatic summarization. It translates the capacity of a term to discriminate a document within a collection, or a part of a document within a whole document. This paper deals with term weighting strategies in the context of lexical cohesion based topic segmentation. The aim is to propose a term weighting method which does not require any external information data. Weights are estimated from the content itself which is considered as a collection of mono-thematic documents. Two approaches are proposed and significant improvements are observed on a rich corpus covering various formats of Broadcast News shows from 8 French TV channels.

Sentiment classification (SC) is an ongoing field of research, which involves computing opinions, sentiments, and the subjectivity of a text. It has recently been proven that imbalanced classification is challenging for the SC research community. Most existing studies assume that the balance between negative and positive samples may not be true in reality. This work describes a method to improve the problem of imbalanced sentiment classification using supervised term weighting schemes and shows how these weighting schemes can improve the performance of sentiment classification with imbalanced data, especially in the domain of multi-class classification. Nonetheless, to obtain the most appropriate term weighting schemes, five term weighting schemes are comparatively studied, namely tf-idf, tf-idf-icf, tf-rf, tf-igm, and sqrt_tf-igm. In addition to comparing several term weightings schemes, this work also compares four supervised machine learning algorithms to obtain an appropriate algorithm, including k-Nearest Neighbor (k-NN), Multinomial Naïve Bayes (MNB), Support Vector Machines (SVM) with linear, and SVM with RBF. After evaluating by F1, the performance of sqrt_tf-igm was superior to all other weighting schemes. Since the overall picture of sqrt_tf-igm returned better results than the tf-idf, tf-idf-icf, and tf-rf methods, with improved scores of F1 at 10.94%. Meanwhile, the result of sqrt_tf-igm was slightly better than tf-igm.

Term weighting strategy plays an essential role in the areas related to text processing such as text categorization and information retrieval. In such systems, term frequency, inverse document frequency, and document length normalization are important factors to be considered when a term weighting strategy is developed. Term length normalization is proposed to give equal opportunities to retrieve both lengthy documents and shorter ones. However, terms in very short documents that may be useless for users, especially in the scenario of Web information retrieval, could be assigned very high weights, resulting in a situation where shorter documents are ranked higher than lengthy documents that are more relevant to users information needs. In this research, a new R-tfidf term weighting strategy is proposed to alleviate the side effects of document length normalization. Experimental results demonstrate the proposed approach can to some extent improve the performance of text categorization.

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize the mTFIDF that takes into account the missing terms and the number of the documents in which the term appears when calculating the term weight. For further robust term weight, we combine mTFIDF with two weighting methods. We evaluate CmTLB against the studied weighting schemes by the quality of the learned topics (topic visualization and topic coherence), classification, and clustering tasks. We applied weighting schemes to Latent Dirichlet allocation (LDA) and Dirichlet multinomial mixture (DMM) on eight Arabic long and short document datasets, respectively. The experiment results outline that appropriate weighting schemes can effectively improve topic modeling performance on Arabic texts. More importantly, our proposed CmTLB significantly outperforms the other weighting schemes. Secondly, we investigate whether the Arabic stemming process can improve topic modeling performance. We study the three approaches of Arabic stemming including root-based, stem-based, and statistical approaches. We also train topic models with weighting schemes on documents after applying four stemmers related to different stemming approaches. The results outline that applying the stemming process not only reduces the dimensionality of term-document matrix leading to fast estimation process, but also show enhancement of topic modeling performance both on short and long Arabic documents. Moreover, Farasa stemmer achieves the highest performance in most cases, since it prevents the ambiguity that may happen because of the blind removal of the affixes such as in root-based or stem-based stemmers.

A study of supervised term weighting scheme for sentiment analysis

The term weighting scheme, which is used to convert documents into vectors in the term spaces, is a vital step in automatic text categorization. The previous studies showed that term weighting schemes dominate the performance rather than the kernel functions of SVMs for the text categorization task. In this paper, we conducted experiments to compare various term weighting schemes with SVM on two widely-used benchmark data sets. We also presented a new term weighting scheme tf.rf for text categorization. The cross-scheme comparison was performed by using McNemar's tests. The controlled experimental results showed that the newly proposed tf.rf scheme is significantly better than other term weighting schemes. Compared with schemes related with tf factor alone, the idf factor does not improve or even decrease the term's discriminating power for text categorization. The binary and tf.chi representations significantly underperform the other term weighting schemes.

Supervised learning methods are widely used in text sentiment classification. To acquire high classification performance, the effective and precise term weighting scheme plays a prime and necessary role for classification system. The traditional term weighting schemes often ignore the use of the available labeling information as the prior knowledge, which results the expressed relationships between features and class labels are not accurate and adequate enough. Hence, this paper proposes a term weighting scheme which makes use of class contribution of feature terms, and obtains a scheme which takes into account the class contribution, local distribution and global distribution of feature terms. The scheme expresses the relationships between features and class labels as a class contribution degree factor which is based on posterior probability, then combines it with the factors of frequency and inverse document frequency. Compared with a series of term weighting schemes, experimental results on a widely used dataset show that the proposed scheme can significantly improve the performance of sentiment classification.

Now finding relevant information is absolutely needed by users of the information. Actually, finding relevant information is more difficult than just looking for information. The presence information retrieval (IR) system is a system for automatically searching the relevant information to the user needs, which is expressed in the required query, and this query will be used as an input and processed by IR system and then will get relevant documents to the required query. One of methods of finding relevant information by query is term weighting by both query and document. The commonly used methods are the term weighting of local and global weighting, while the local term weighting algorithms that are used such as TF, TF logarithmic, binary TF and augmented TF and global weighting is IDF, IDFP, IDFB. In this research, besides of comparison of term weighting algorithms, it is also proposed new improvement in term weighting algorithms by adding global weight value that changes base 10 logarithmic to base 2 logarithmic and adds the number 1 in the global term weighting IDFP. Measurements result of this research are Precision, Recall and NIAP. In This research, the best result for precision and NAIP have been performed by new propose term weighting algorithms. After compare and analyze the result of the weighting algorithms. It is necessary to combine the weighting with semantic algorithms so that it will get better result.

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Multi-Tier Attention Network using Term-weighted Question Features for Visual Question Answering

Illicit Web content filtering is a content-based analysis technique, applied to censor inappropriate contents on the Internet. Web content filtering can recognize undesirable contents through the application of AI techniques, linguistic analysis, or machine learning to classify Web pages into a set of predefined categories. However, the capacity to distinguish between useful and harmful Web content remains a major research challenge, which usually leads to the problem of under-blocking and over-blocking. Further, the extraction of best term representation for classifier presents a major limitation due to curse of dimensionality, where a feature can have the same term frequency (TF) in two or more categories but has different semantic meanings such as illicit pornography and sex education context also known as ambiguous issues. Besides, the high dimensionality of features on a Web page, even for moderate size, it has made the term representation value for classifier more complex, which affects the performance of classification. Thus, this research proposes a modified term weighting scheme (TWS) for narrative and discrete Web in order to increase the classification performance. Characteristics of pornography Web site were extracted and significant characteristics were identified and mapped against term weighting factors. Initial result revealed that other criteria such as rare feature have potential to be regarded as significant criteria in TWS technique to distinguish high-similarity Web content.

In this paper we propose a path expression-based smoothing method of query likelihood model for XML element retrieval (QLMER). Though the query likelihood model, one of the statistical language models, is regarded as an accurate term weighting scheme in document retrieval, it has not been surveyed enough in XML element retrieval. Some term Weighting schemes for XML element retrieval utilize the idea of a path expression and it is effective for accurate retrieval. Therefore, we propose the path expression-based smoothing method. We are also interested in the potential power of QLMER compared with a commonly used term weighting scheme, BM25E, which is a classic probabilistic model. Our experimental evaluations showed that the proposed smoothing method is more effective than the existing one. In addition, BM25E is more effective than QLMER even though the effectiveness improved with the proposed method.

Discourse anaphora in Peninsular Spanish interactions: Signalling alignment through anaphor selection