Interactive Document Indexing Method Based on Explicit Semantic Analysis

Janusz, Andrzej; Świeboda, Wojciech; Krasuski, Adam; Nguyen, Hung Son

doi:10.1007/978-3-642-32115-3_18

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…In order to make it possible, the higher-level features of textual documents that represent relevant aspects of their semantics are extracted using a combination of Explicit Semantic Analysis (ESA) [38,129] and a novel notion of information bireduct [29,36,170]. Due to the utilization of the information bireducts, the evaluation of similarity in the unsupervised RBS model can be interpreted as an interaction between artificial agents who are characterised by different experience and preferences, and thus have different views on semantics of the compared documents.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, if experts could provide feedback in the form of manually labelled exemplary documents, some supervised learning techniques can also be employed in that task [38]. However, particular methods for automatic tagging of textual data are not in the scope of this research.…”

Section: Relational Patterns Learningmentioning

confidence: 99%

“…Among them, the most important considered the problem of feature selection and learning with ensembles of single and multi-label classifiers [30][31][32][33][34][35][36][37]. Moreover, the research on unsupervised version of Rule-Based Similarity was largely influenced by the author's previous work on the semantic information retrieval and Explicit Semantic Analysis, which was conducted within the SYNAT project [38][39][40].…”

Section: Main Contributionsmentioning

confidence: 99%

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Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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Abstract. The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author's PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky's feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Relational Patterns Learningmentioning

confidence: 99%

Section: Main Contributionsmentioning

confidence: 99%

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Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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“…Since the model resembles a neural network, in our previous research [8] we proposed to use a simple learning algorithm for the adaptation of weights from the inverted index to a feedback regarding the tagging quality, obtained from domain experts. The algorithm was based on a typical perceptron learning schema, namely the error backpropagation approach [14].…”

Section: The Adaptive Learning Algorithmmentioning

confidence: 99%

“…Within the SYNAT project, some successful methods for the semantic text representation and indexing have already been developed [4], [8]. In this paper we discuss an adaptive learning model of terms-to-concepts associations which can be treated as an extension of the Explicit Semantic Analysis (ESA) method [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning for Improving Semantic Tagging of Scientific Articles

Janusz

Stawicki

Nguyen

2014

Annals of Computer Science and Information Systems

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Abstract-In this paper we consider a problem of automatic labeling of textual data with concepts explicitly defined in an external knowledge base. We describe our tagging system and we also present a framework for adaptive learning of associations between terms or phrases from the texts and the concepts. Those associations are then utilized by our semantic interpreter, which is based on the Explicit Semantic Analysis (ESA) method, in order to label scientific articles indexed by our SONCA platform. Apart from the description of the learning algorithm, we show a few practical application examples of our system, in which it was used for tagging scientific articles with headings from the MeSH ontology, categories from ACM Computing Classification System and from OECD Fields of Science and Technology Classification.

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An Approach to Semantic Indexing Based on Tolerance Rough Set Model

Nguyen

2013

Advanced Computational Methods for Knowledge Engineering

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Interactive Document Indexing Method Based on Explicit Semantic Analysis

Cited by 16 publications

References 10 publications

Algorithms for Similarity Relation Learning from High Dimensional Data

Algorithms for Similarity Relation Learning from High Dimensional Data

Adaptive Learning for Improving Semantic Tagging of Scientific Articles

An Approach to Semantic Indexing Based on Tolerance Rough Set Model

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