The method of organization of word meanings is a crucial issue with lexical databases. Our purpose in this research is to extract word hierarchies from corpora automatically. Our initial task to this end is to determine adjective hyperonyms. In order to find adjective hyperonyms, we utilize abstract nouns. We constructed linguistic data by extracting semantic relations between abstract nouns an...

Extracting valuable data among large volumes of data is one of the main challenges in Big Data. In this paper, a Hierarchical Multi-Label Classification process called Semantic HMC is presented. This process aims to extract valuable data from very large data sources, by automatically learning a label hierarchy and classifying data items.The Semantic HMC process is composed of five scalable step...

This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using sta...

In this paper we consider the problem of dimensionality reduction techniques. Two techniques such as Independent Component analysis (ICA) and multidimensional latent semantic analysis (MDLSA) are proposed. A new document analysis method named multidimensional latent semantic analysis (MDLSA) which resolves the problem of in-depth document analysis, mines local information from a document effici...

Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenome...

Prior research in scene classi cation has focused on mapping a set of classic low-level vision features to semantically meaningful categories using a classi er engine. In this paper, we propose improving the established paradigm by using a simpli ed low-level feature set to predict multiple semantic scene attributes that are integrated probabilistically to obtain a nal indoor/outdoor scene clas...

Many semantic portals use faceted browsing, where the facets are based on the underlying indexing ontologies of the content. However, in many cases, like in medical applications, the ontologies may be very large and complex, and do not provide the end-user with intuitive facet hierarchies for conceptualizing the content, for formulating queries, and for classifying the search results. We argue ...

Classifying 3D models into classes is an important step in 3D model retrieval process. However, most classification system does not include semantic information. In this paper, a new method has been proposed to classify and to retrieve 3D model using semantic concepts and ontology. First, we use the machine learning methods to label 3D models by k-means algorithm in shape indexes space. Second,...

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task, which aims to extract useful information from unstructured textual data by detecting and classifying Named Entity (NE) phrases into predefined semantic classes. This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and sho...

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of relations between pairs of words in a text. We present an evaluation task designed to provide a framework for comparing different approaches to classifying semantic relations between nominals in a sentence. This is part of SemEval, the 4 edition of the semantic evaluation event previo...