This article describes a new approach of HTML pages search via Internet, which is based on the semantic understanding of pages content by means of multi-agent technology. Multi-agent text understanding system, which is the basis of the approach, converts an input query and pages, received from conventional search engines, to formalized semantic descriptors, and evaluates similarity of these des...

In this paper we describe a system for semantic interpretation of noun compounds that relies on world and domain knowledge from a knowledge base. This architecture combines domain-independent compounding rules with a task-independent knowledge representation, allowing both components to be flexibly reused. We present examples from Scientific American text, on which the system was developed, and...

In this paper, we will propose TeLQAS, which is an ontology-based natural language question/answering system for the domain of Telecommunication Technologies. In an online process, the system accepts the users’ questions in English, and after retrieving the related text documents from either its local database or web; it summarizes the retrieved text documents with the highest relevance. The pr...

The advent of computing has exacerbated the problem of overwhelming information. Advanced information management strategies such as Information Extraction, Information Filtering, Information Retrieval, and Text Categorization are becoming important to manage the deluge of information. Information Extraction (IE) systems can be used to automatically extract relevant information from free-form te...

Concept mining (CM) is the area of exploring and finding links, associations, relationships, and patterns among huge collections of information. In this paper, we propose concept-based text representation, with an emphasis on using the proposed representation in different application s such as information retrieval, text summarization, and question answering. This work presents a new paradigm f...

The TIPSTER architecture is a domain-specific software architecture (DSSA) for the text processing domain. The primary goal of the architecture was to allow the use of standardize text processing components, enabling "plug and play" capabilities of the various tools being developed. This would permit the sharing of software among the various research efforts and operational prototype applications.

We examined a method for extracting the low frequency important single-word terms from domain specific text. Firstly, domain-relevant fragments were extracted from the text with the help of a dependency tree. Then the fragments were clustered and candidate terms were defined using the semantic classifier. The studies suggest that this approach allows extracting even terms with a single occurrence.

In general, research related to text analysis assumes that the information contained in text form, although ambiguous, is correct with respect to the domain to which that text belongs to. This assumption comes in part from the fact that text analysis has historically been done over scientific documents. As the trend of taking text understanding to broader domains, such as Internet, we need to c...

Aiming at more efficient search on the Internet, it seems adequate to deploy classification techniques using semantic resources in order to restrict this search to the user's domain of interest. In this work, we try to assess the impact of integrating semantic knowledge on text classification. This integration can be realized in different ways. The one we choose in this paper is text conceptual...

background: protein-protein interactions do not provide any direct information re­garding the domains within the proteins that mediate the interactions. the majority of proteins are multi domain proteins and the interaction between them is often defined by the pairs of their domains. most of the former studies focus only on interacting do­main pairs. however they do not consider the interaction...