Content-Aware DataGuides for Indexing Large Collections of XML Documents

XML is well-suited for modelling structured data with textual content. However, most indexing approaches perform structure and content matching independently, combining the retrieved path and keyword occurrences in a third step. This paper shows that retrieval in XML documents can be accelerated significantly by processing text and structure simultaneously during all retrieval phases. To this end, the Content-Aware DataGuide (CADG) enhances the wellknown DataGuide with (1) simultaneous keyword and path matching and (2) a precomputed content/structure join. Extensive experiments prove the CADG to be 50-90% faster than the DataGuide for various sorts of query and document, including difficult cases such as poorly structured queries and recursive document paths. A new query classification scheme identifies precise query characteristics with a predominant influence on the performance of the individual indices. The experiments show that the CADG is applicable to many real-world applications, in particular large collections of heterogeneously structured XML documents

On a Topic Model for Sentences

Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as sentences, contains much information which is generally lost with these models. In this paper, we propose sentenceLDA, an extension of LDA whose goal is to overcome this limitation by incorporating the structure of the text in the generative and inference processes. We illustrate the advantages of sentenceLDA by comparing it with LDA using both intrinsic (perplexity) and extrinsic (text classification) evaluation tasks on different text collections

Probability of Semantic Similarity and N-grams Pattern Learning for Data Classification

Semantic learning is an important mechanism for the document classification, but most classification approaches are only considered the content and words distribution. Traditional classification algorithms cannot accurately represent the meaning of a document because it does not take into account semantic relations between words. In this paper, we present an approach for classification of documents by incorporating two similarity computing score method. First, a semantic similarity method which computes the probable similarity based on the Bayes' method and second, n-grams pairs based on the frequent terms probability similarity score. Since, both semantic and N-grams pairs can play important roles in a separated views for the classification of the document, we design a semantic similarity learning (SSL) algorithm to improves the performance of document classification for a huge quantity of unclassified documents. The experiment evaluation shows an improvisation in accuracy and effectiveness of the proposal for the unclassified documents

Classification of Under-Resourced Language Documents Using English Ontology

Automatic documents classification is an important task due to the rapid growth of the number of electronic documents, which aims automatically assign the document to a predefined category based on its contents. The use of automatic document classification has been plays an important role in information extraction, summarization, text retrieval, question answering, e-mail spam detection, web page content filtering, automatic message routing , etc.Most existing methods and techniques in the field of document classification are keyword based, but due to lack of semantic consideration of this technique, it incurs low performance. In contrast, documents also be classified by taking their semantics using ontology as a knowledge base for classification; however, it is very challenging of building ontology with under-resourced language. Hence, this approach is only limited to resourced language (i.e. English) support. As a result, under-resourced language written documents are not benefited such ontology based classification approach. This paper describes the design of automatic document classification of under-resourced language written documents. In this work, we propose an approach that performs classification of under-resourced language written documents on top of English ontology. We used a bilingual dictionary with Part of Speech feature for word-by-word text translation to enable the classification of document without any language barrier. The design has a concept-mapping component, which uses lexical and semantic features to map the translated sense along the ontology concepts. Beside this, the design also has a categorization component, which determines a category of a given document based on weight of mapped concept. To evaluate the performance of the proposed approach 20-test documents for Amharic and Tigrinya and 15-test document for Afaan Oromo in each news category used. In order to observe the effect of incorporated features (i.e. lemma based index term selection, pre-processing strategies during concept mapping, lexical and semantics based concept mapping) five experimental techniques conducted. The experimental result indicated that the proposed approach with incorporation of all features and components achieved an average F-measure of 92.37%, 86.07% and 88.12% for Amharic, Afaan Oromo and Tigrinya documents respectively. Keywords: under-resourced language, Multilingual, Documents or text Classification, knowledge base, Ontology based text categorization, multilingual text classification, Ontology. DOI: 10.7176/CEIS/10-6-02 Publication date:July 31st 201

A Multi-Modal Multilingual Benchmark for Document Image Classification

Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.Comment: Accepted to EMNLP 2023 (Findings