Understanding the user's intention is crucial in human-machine interaction. When dealing with text input, Word Sense Disambiguation (WSD) techniques play an important role. WSD techniques typically require well-formed sentences as context to operate, ...

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text ...

Both parts of this paper discuss the basic aspects of semantic computing, semantic technologies, and semantic applications applied to NL-text big data processing for knowledge extracting and decision making. The basic components of the ...

Publications on the Web can influence the public opinion about certain entities (e.g., politicians, institutions). At the same time, a variety of indicators can be extracted from these publications and used to estimate entity performance (e.g., ...

This article introduces Cartograph, a visualization system that harnesses the vast world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic ...

Identifying relationships between requirements described in natural language (NL) is a difficult task in requirements engineering (RE). This paper presents a novel approach that uses Semantic Frames in FrameNet to find the relationships between ...

Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no ...

While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits ...

With the aid of recently proposed word embedding algorithms, the study of semantic relatedness has progressed and advanced rapidly. In this research, we propose a novel structural-fitting method that utilizes the linguistic ontology into vector space ...

A large software project usually has lots of various textual learning resources about its API, such as tutorials, mailing lists, user forums, etc. Text retrieval technology allows developers to search these API learning resources for related documents ...

As advanced technologies, such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive or privilege review. Keywords are conceptually easy to ...

This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using ...

This paper addresses the task of document retrieval based on the degree of document relatedness to the meanings of a query by presenting a semantic-enabled language model. Our model relies on the use of semantic linking systems for forming a graph ...

Assessment of semantic similarity between concepts is of great importance in many applications dealing with textual data, such as natural language processing, knowledge acquisition, document semantic annotation and information retrieval systems. ...

Financial documents are omnipresent and necessitate extensive human efforts in order to extract, validate and export their content. Considering the high importance of such data for effective business decisions, the need for accuracy goes beyond any ...

Recent research has explored the use of Knowledge Bases (KBs) to represent documents as subgraphs of a KB concept graph and define metrics to characterize semantic relatedness of documents in terms of properties of the document concept graphs. However, ...

Semantic relatedness deals with the problem of measuring how much two words are related to each other. While there is a large body of research for developing new measures, the use of semantic relatedness SR measures in topic segmentation has not been ...

While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet or Wikitionary, the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform ...

GloVe, global vectors for word representation, performs well in some word analogy and semantic relatedness tasks. However, we find that some dimensions of the trained word embedding are abnormal. We verify our conjecture via removing these abnormal ...

Although tagging simplifies resource browsing and retrieval, it suffers from several issues: among them are redundancy and ambiguity. In this work we focus on the problem of resolving tag word-sense ambiguity within a typical semi-automatic tagging ...