نتایج جستجو برای: linguistic term set

تعداد نتایج: 1248615  

2002
Thierry Declerck

In this position paper we describe the actual state of the development of an integrated set of tools (called SCHUG) for language processing supporting interaction with disparate sources of information, making thus Natural Language Processing (NLP) and Human Language Technology (HLT) even more relevant for Information Technology (IT) applications. The set of tools is realizing the communication ...

2002
Enrique Herrera-Viedma Oscar Cordón Juan Carlos Herrera María Luque

An information retrieval system (IRS) based on fuzzy multi-granular linguistic information is proposed. The system has an evaluation method to process multi-granular linguistic information, in such a way that the inputs to the IRS are represented in a different linguistic domain than the outputs. The system accepts Boolean queries whose terms are weighted by means of the ordinal linguistic valu...

Journal: :European Journal of Operational Research 2008
Yucheng Dong Yin-Feng Xu Hongyi Li

Inspired by the concept of deviation measure between two linguistic preference relations, this paper further defines the deviation measure of a linguistic preference relation to the set of consistent linguistic preference relations. Based on this, we present a consistency index of linguistic preference relations and develop a consistency measure method for linguistic preference relations. This ...

Journal: :Kybernetika 2013
Mohammed-Amine Abchir Isis Truck

In the domain of Computing with words (CW), fuzzy linguistic approaches are known to be relevant in many decision-making problems. Indeed, they allow us to model the human reasoning in replacing words, assessments, preferences, choices, wishes. . . by ad hoc variables, such as fuzzy sets or more sophisticated variables. This paper focuses on a particular model: Herrera & Mart́ınez’ 2-tuple lingu...

2010
Cristina Alcalde Ana Burusco Ramón Fuentes-González

In this work, we analyze how the linguistic labels of a linguistic variable can be a useful tool in the L-Fuzzy Concept Theory. In concrete, we study the L-Fuzzy concepts obtained from a departure set represented by means of these linguistic labels applied to the set of objects or attributes. We also illustrate the results by means of an example.

2017
Irina Ovchinnikova Liana Ermakova Josiane Mothe

This paper offers a linguistic approach to the study of the potency of query expansion while retrieving information from the web. The expansion allows enhancing the results; however, some queries show lower effectiveness after expansion. The objective of the study is to analyze linguistic features of initial query (IQ) as predictors for the expansion potency by different systems. The IQ is cons...

2006
Paul Buitelaar Thierry Declerck Anette Frank Stefania Racioppa Malte Kiesel Michael Sintek Ralf Engel Massimo Romanelli Daniel Sonntag Berenike Loos Vanessa Micelli Robert Porzel Philipp Cimiano

To allow for a direct connection of this linguistic information for terms with corresponding classes and properties in a domain ontology, we developed a lexicon model (LingInfo) that enables the definition of LingInfo instances (each of which represents a term) for each class or property. The LingInfo model is represented by use of a meta-class, which allows for the representation of LingInfo i...

Journal: :JASIS 1993
Gloria Bordogna Gabriella Pasi

The generalization of Boolean lnformatlon Retrieval Systems (IRS) is still an open research field; in fact, though such systems are diffused on the market, they present some limitations; one of the main features lacking in these systems is the ability to deal with the “imprecision” and “subjectivity” characterizing retrieval activity. However, the replacement of such systems would be much more ...

1998
Pascale Fung

Key term extraction is very useful for information retrieval. Most term extraction methods use one of two approaches, namely lexical and grammatical. We argue that due to the diierences in linguistic and character set characteristics of Chinese and Japanese, a lexical approach is more suitable for Chinese whereas a grammatical approach is more suitable for Japanese. In this paper, we present tw...

Journal: :Journal of Experimental Psychology: Human Perception and Performance 2009

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function paginate(evt) { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term pg=parseInt(evt.target.text) var data={ "year":filter_year, "term":term, "pgn":pg } filtered_res=post_and_fetch(data,url) window.scrollTo(0,0); } function update_search_meta(search_meta) { meta_place=document.getElementById("search_meta_data") term=search_meta.term active_pgn=search_meta.pgn num_res=search_meta.num_res num_pages=search_meta.num_pages year=search_meta.year meta_place.dataset.term=term meta_place.dataset.page=active_pgn meta_place.dataset.num_res=num_res meta_place.dataset.num_pages=num_pages meta_place.dataset.year=year document.getElementById("num_result_place").innerHTML=num_res if (year !== "unfilter"){ document.getElementById("year_filter_label").style="display:inline;" document.getElementById("year_filter_place").innerHTML=year }else { document.getElementById("year_filter_label").style="display:none;" document.getElementById("year_filter_place").innerHTML="" } } function update_pagination() { search_meta_place=document.getElementById('search_meta_data') num_pages=search_meta_place.dataset.num_pages; active_pgn=parseInt(search_meta_place.dataset.page); document.getElementById("pgn-ul").innerHTML=""; pgn_html=""; for (i = 1; i <= num_pages; i++){ if (i===active_pgn){ actv="active" }else {actv=""} pgn_li="
  • " +i+ "
  • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->