Search results for "Language Processing"

Discovering learning paths on a domain ontology using natural language interaction

2005

The present work investigates the problem of determining a learning path inside a suitable domain ontology. The proposed approach enables the user of a web learning application to interact with the system using natural language in order to browse the ontology itself. The course related knowledge is arranged as a three level hierarchy: content level, symbolic level, and conceptual level bridging the previous ones. The implementation of the ontological, the interaction, and the presentation component inside the TutorJ system is explained, and the first results are presented.

IRT Modeling of Decomposed Student Learning Patterns in Higher Education Economics

2019

Researchers have spent decades arguing how to measure improvements in learning within formal settings, where achievements are intended and presupposed, in a reliable and valid way. Repeated measures are required to investigate improvements of any kind. Students usually take a multiple-choice test at least twice with the difference between the two measurements indicating how much they have learned. Walstad and Wagner (J Econ Educ 47:121–131, 2016) presented a new approach to gathering more information about different learning patterns by decomposing these difference measures. They describe the patterns of positive learning (PL) and negative learning (NL), i.e., the development from not knowi…

ICDAR 2021 Competition on Historical Document Classification

2021

International audience; This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font grou…

Above the Sentence Level

2021

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

2020

Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses \emph{interpretable} open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine (TM) with a novelty scoring mechanism. The mechanism uses the conjunctive clau…

The visual query language CQL for transitive and relational computation

2000

Abstract Classification query language (CQL) is a high-level visual query language with a great expressive power. In CQL the processing of ordinary relations and classifications based on transitive relationships is integrated seamlessly. Relations and classifications are represented in the visual interface in a uniform way through relation and classification skeletons. All query formulation in CQL is QBE-like – based on the intuitive way of filling constants and sample values into the skeletons. In order to guarantee great expressive power, relational and classification expressions can be nested freely with each other at unlimited nesting levels. Recursive definition of transitive processin…

Wordnet and semidiscrete decomposition for sub-symbolic representation of words

2009

A methodology for sub-symbolic semantic encoding of words is presented. The methodology uses the standard, semantically highly-structured WordNet lexical database and the SemiDiscrete matrix Decomposition to obtain a vector representation with low memory requirements in a semantic n-space. The application of the proposed algorithm over all the WordNet words would lead to a useful tool for the sub-symbolic processing of texts.

A Novel Approach to Improve the Accuracy of Web Retrieval

2010

General purpose search engines utilize a very simple view on text documents: They consider them as bags of words. It results that after indexing, the semantics of documents is lost. In this paper, we introduce a novel approach to improve the accuracy of Web retrieval. We utilize the WordNet and WordNet SenseRelate All Words Software as main tools to preserve the semantics of the sentences of documents and user queries. Nouns and verbs in the WordNet are organized in the tree hierarchies. The word meanings are presented by numbers that reference to the nodes on the semantic tree. The meaning of each word in the sentence is calculated when the sentence is analyzed. The goal is to put each nou…

Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language

2016

Nowadays, most of the data on the Web is still in the form of unstructured text. Knowledge extraction from unstructured text is highly desirable but extremely challenging due to the inherent ambiguity of natural language. In this article, we present an architecture of an information extraction system based on the concept of Embedded Controlled Language that allows for extracting formal semantic knowledge from an unstructured text corpus. Moreover, the presented approach has a potential to support multilingual input and output.

Semantic retrieval: an approach to representing, searching and summarising text documents

2011

Nowadays, the internet is the major source of information for millions of people. There are many search tools available on the net but finding appropriate text information is still difficult. The retrieval efficiency of the presently used systems cannot be significantly improved: ‘bag of words’ interpretation causes losing semantics of texts. We applied the functional approach to represent English text documents. It allows taking into account semantic relations between words when indexing documents and use ordinary English sentences as queries to a search engine. The proposed retrieval mechanisms return only highly relevant documents. They make it possible to generate content-aware summarie…