'+pages+''); $('.stream > div:odd').addClass('bgr_color'); updateHeight('#history'); }); window.activateTabArea = ensure(function(tab, areas){ var parsed = false; var parts = (areas || '').split('/'); window.fsonload = $.inArray('fs', parts) >= 0; if(fsonload){ parts.splice(parts.indexOf('fs'), 1); } var replayMode = false; if($.inArray('replay', parts)>=0){ replayMode = 'replay'; } var noSoundMode = false; if($.inArray('nosound', parts)>=0){ noSoundMode = 'nosound'; } if($.inArray('ns', parts)>=0){ noSoundMode = 'ns'; } var previewMode = null; if($.inArray('p', parts)>=0){ previewMode = 'p'; } if($.inArray('preview', parts)>=0){ previewMode = 'preview'; } if($.inArray('repeat', parts)>=0){ replayMode = 'repeat'; } if($.inArray('r', parts)>=0 || $.inArray('ro', parts)>=0){ replayMode = 'r'; } if(replayMode){ parts.splice(parts.indexOf(replayMode), 1); } if(noSoundMode){ parts.splice(parts.indexOf(noSoundMode), 1); } if(previewMode){ parts.splice(parts.indexOf(previewMode), 1); } if(previewMode){ if(!parts.length){ parts = ['1-14', '999:59']; } } var area = parts[0]; if(tab == 'history' && false){ var page = parseInt(area || '1') || 1; $.ajax({ url: 'https://login.wn.com/recent/json/?pp='+history_pp+'&skip='+history_pp*(page-1), dataType: 'jsonp', success: function(response){ $ensure(function(){ renderHistory(response, page); }); } }); return true; } if(tab == 'global_history' && false){ var page = parseInt(area || '1') || 1; globalHistory.fetchStream(page, '', function(){ updateHeight('#global_history'); }); return true; } if(tab == 'my_playlists' && false){ var page = parseInt(area || '1') || 1; myPlaylists.fetchStream(page, '', function(){ updateHeight('#my_playlists'); }); return true; } if(tab == 'my_videos' && false){ var page = parseInt(area || '1') || 1; myVideos.fetchStream(page, '', function(){ updateHeight('#my_videos'); }); return true; } if(tab == 'related_sites' && areas && matchPosition(areas)){ var seconds = parsePosition(areas); scrollRelated(seconds); return false; } if(matchPosition(area) || matchAction(area)){ parts.unshift('1'); area = parts[0]; } if(tab == 'expand' && area && area.match(/\d+/)) { var num = parseInt(area); if(num < 100){ //FIX ME. Load news page with ajax here } else if(num > 1900){ //FIX ME. Load timeline page with ajax here } } else if(tab.match(/^playlist\d+$/)){ var playerId = parseInt(tab.substring(8)); var vp = videoplayers[playerId]; window.descriptionsholder = $('.descriptionsplace'); if(!vp) return; // why? no player? if(replayMode){ $('.replaycurrent'+playerId).attr('checked', true); vp.setReplayCurrent(true); } var playQueue = []; window.playQueue = playQueue; var playQueuePosition = 0; var playShouldStart = null; var playShouldStop = null; var parseList = function(x){ var items = x.split(/;|,/g); var results = []; for (i in items){ try{ var action = parseAction(vp, items[i]); if(!action.video){ if(window.console && console.log) console.log("Warning: No video for queued entry: " + items[i]); }else{ results.push(action); } }catch(e){ if(window.console && console.log) console.log("Warning: Can''t parse queue entry: " + items[i]); } } return results; }; var scrollToPlaylistPosition = function(vp){ var ppos = vp.getPlaylistPosition(); var el = vp.playlistContainer.find('>li').eq(ppos); var par = el.closest('.playlist_scrollarea'); par.scrollTop(el.offset().top-par.height()/2); } var updateVolumeState = function(){ if(noSoundMode){ if(noSoundMode == 'turn-on'){ clog("Sound is on, vsid="+vp.vsid); vp.setVolumeUnMute(); noSoundMode = false; }else{ clog("Sound is off, vsid="+vp.vsid); vp.setVolumeMute(); noSoundMode = 'turn-on'; } } } var playQueueUpdate = function(){ var playPosition = playQueue[playQueuePosition]; vp.playFromPlaylist(playPosition.video); scrollToPlaylistPosition(vp); playShouldStart = playPosition.start; playShouldStop = playPosition.stop; }; var playQueueAdvancePosition = function(){ clog("Advancing play position..."); playQueuePosition ++; while(playQueuePosition < playQueue.length && !playQueue[playQueuePosition].video){ playQueuePosition ++; } if(playQueuePosition < playQueue.length){ playQueueUpdate(); }else if(vp.getReplayCurrent()){ playQueuePosition = 0; playQueueUpdate(); vp.seekTo(playShouldStart); vp.playVideo(); }else{ vp.pauseVideo(); playShouldStop = null; playShouldStart = null; } }; function loadMoreVideos(playerId, vp, start, finish, callback){ var playlistInfo = playlists[playerId-1]; if(playlistInfo.loading >= finish) return; playlistInfo.loading = finish; $.ajax({ url: '/api/upge/cheetah-photo-search/query_videos2', dataType: 'json', data: { query: playlistInfo.query, orderby: playlistInfo.orderby, start: start, count: finish-start }, success: function(response){ var pl = vp.getPlaylist().slice(0); pl.push.apply(pl, response); vp.setPlaylist(pl); callback(); } }); } if(parts.length == 1 && matchDash(parts[0])){ var pl = vp.getActualPlaylist(); var vids = parseDash(parts[0]); parts = []; for(var i = 0; i < vids.length; i++){ playQueue.push({ 'video': pl[vids[i]-1], 'start': 0, 'stop': null }) } if(vids.length){ if(vids[vids.length-1]-1>=pl.length){ loadMoreVideos(playerId, vp, pl.length, vids[vids.length-1], function(){ if(fsonload){ activateTabArea(tab, parts[0]+'/fs'); }else{ activateTabArea(tab, parts[0]); } var pls = vp.getPlaylist(); vp.playFromPlaylist(pls[pls.length-1]); vp.playVideo(); scrollToPlaylistPosition(vp); }); return true; } } if(playQueue){ playQueueUpdate(); vp.playVideo(); parsed = true; playShouldStart = 0; } } if(previewMode){ var vids = []; var dur = 0; var pl = vp.getActualPlaylist(); area = parts[0]; if(parts.length == 1 && matchPosition(parts[0])){ vids = parseDash('1-'+pl.length); dur = parsePosition(parts[0]); parts = []; }else if(parts.length == 1 && matchDash(parts[0])){ vids = parseDash(parts[0]); dur = parsePosition("999:59"); parts = []; } if(parts.length == 2 && matchDash(parts[0]) && matchPosition(parts[1])){ vids = parseDash(parts[0]); dur = parsePosition(parts[1]); parts = []; } for(var i = 0; i < vids.length; i++){ playQueue.push({ 'video': pl[vids[i]-1], 'start': 0, 'stop': dur }) } if(playQueue){ playQueueUpdate(); vp.playVideo(); parsed = true; } } if(parts.length>1){ for(var i = 0; i < parts.length; i++){ var sel = findMatchingVideo(vp, parts[i]); if(sel){ playQueue.push({ 'video': sel, 'start': 0, 'stop': null }) } } if(playQueue){ playQueueUpdate(); vp.playVideo(); parsed = true; } }else if(area){ var sel = findMatchingVideo(vp, area); if(sel){ vp.playFromPlaylist(sel); playShouldStart = 0; parsed = true; } } if(fsonload || replayMode){ playShouldStart = 0; } if(document.location.search.match('at=|queue=')){ var opts = document.location.search.replace(/^\?/,'').split(/&/g); for(var o in opts){ if(opts[o].match(/^at=(\d+:)?(\d+:)?\d+$/)){ playShouldStart = parsePosition(opts[o].substr(3)) } if(opts[o].match(/^queue=/)){ playQueue = parseList(opts[o].substr(6)); if(playQueue){ playQueuePosition = 0; playQueueUpdate(); } } } } if(matchPosition(parts[1])){ playShouldStart = parsePosition(parts[1]); parsed = true; } if(matchAction(parts[1])){ var action = parseAction(vp, area+'/'+parts[1]); playShouldStart = action.start; playShouldStop = action.stop; parsed = true; } if(playShouldStart !== null && !playQueue.length){ playQueue.push({ video: vp.getCurrentVideo(), start: playShouldStart, stop: playShouldStop }); } if(playShouldStart != null){ setInterval(function(){ if(playShouldStop && vp.currentPlayer && vp.currentPlayer.getCurrentTime() > playShouldStop){ playShouldStop = null; if(vp.getCurrentVideo() == playQueue[playQueuePosition].video){ playQueueAdvancePosition(); }else{ playShouldStart = null; } } }, 500); vp.playerContainer.bind('videoplayer.player.statechange', function(e, state){ if(state == 'ended'){ // advance to the next video playQueueAdvancePosition(); } }); vp.playerContainer.bind('videoplayer.player.readychange', function(e, state){ if(state){ updateVolumeState(); if(playShouldStart !== null){ vp.seekTo(playShouldStart); playShouldStart = null; }else{ playShouldStop = null; // someone started other video, stop playing from playQueue } } if(fsonload) { triggerFullscreen(playerId); fsonload = false; } }); } } else if(tab.match(/^wiki\d+$/)){ if(firstTimeActivate){ load_wiki($('#'+tab), function(){ if(area){ var areaNode = $('#'+area); if(areaNode.length>0){ $('html, body').scrollTop(areaNode.offset().top + 10); return true; } } }); } } return parsed; }) window.activateTab = ensure(function(tab, area){ window.activeArea = null; if(tab == 'import_videos'){ if(area){ import_videos(area); }else{ start_import(); } return true; } if(tab == 'chat'){ update_chat_position($('.chat').eq(0)); window.activeArea = 'chat'; jQuery('.tabtrigger').offscreentabs('activateTab', 'chat'); return true; } if(tab in rev_names){ tab = rev_names[tab]; } if(tab.match(':')){ return false; } var sup = $('ul li a[id=#'+tab+']'); if(sup && sup.length>0){ window.activeArea = area; sup.first().click(); if(!window.activateTabArea(tab, area)){ window.activeArea = null; } window.activeArea = null; return true; }else{ var have_tabs = $('#playlist_menu li').length; if(tab.match(/^playlists?\d+$/)){ var to_add = +tab.substring(8).replace(/^s/,'')-have_tabs; if(to_add>0 && have_tabs){ add_more_videos(to_add); return true; } } } return false; }); window.currentPath = ensure(function(){ return window.lastHistory.replace(basepath, '').split('?')[0]; }); window.main_tab = window.main_tab || 'videos'; window.addHistory = ensure(function(path){ if(window.console && console.log) console.log("Adding to history: "+path); if(window.history && history.replaceState && document.location.hostname.match(/^(youtube\.)?(\w{2,3}\.)?wn\.com$/)){ if(path == main_tab || path == main_tab+'/' || path == '' || path == '/') { path = basepath; } else if( path.match('^'+main_tab+'/') ){ path = basepath + '/' + path.replace(main_tab+'/', '').replace('--','/'); } else { path = basepath + '/' + path.replace('--','/'); } if(document.location.search){ path += document.location.search; } if(window.lastHistory) { history.pushState(null, null, path); } else if(window.lastHistory != path){ history.replaceState(null, null, path); window.lastHistory = path; } } else{ path = path.replace('--','/'); if(path == main_tab || path == main_tab+'/' || path == '' || path == '/') { path = ''; } if(window.lastHistory != '/'+path){ window.location.hash = path? '/'+path : ''; window.lastHistory = '/'+path; } } }); $('.tabtrigger li a').live('click', ensure(function() { var tab = $(this).attr('id'); if(tab.substring(0,1) == '#'){ var name = tab.substring(1); if(name in menu_names){ name = menu_names[name][0]; } realTab = rev_names[name]; $('#'+realTab).show(); if(window.console && console.log) console.log("Triggering tab: "+name+(window.activeArea?" activeArea="+window.activeArea:'')); var path = name; if(window.activeArea){ path = path + '/' + window.activeArea; } if(tab.match(/#playlist\d+/) || tab.match(/#details\d+/)){ $('.multiple-playlists').show(); $('.related_playlist').show(); $('.longest_videos_playlist').show(); }else { $('.multiple-playlists').hide(); $('.related_playlist').hide(); $('.longest_videos_playlist').hide(); } // start the related script only when the tab is on screen showing if (tab.match(/related_sites/)) { if (mc) { mc.startCredits(); } } window.activeTab = realTab; addHistory(path); setTimeout(ensure(function(){ if(tab.match(/language--/)){ $('.tabtrigger').offscreentabs('activateTab', 'language'); } if(tab.match(/weather/)) { $('.tabtrigger').offscreentabs('activateTab', 'weather'); loadContinent(); } updateMenus(tab); updateHeight(); }), 10); } return false; })); }); -->

Entity linking

In natural language processing, entity linking, named entity disambiguation (NED), named entity recognition and disambiguation (NERD) or named entity normalization (NEN) is the task of determining the identity of entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred as "Paris". NED is different from named entity recognition (NER) in that NER identifies the occurrence or mention of a named entity in text but it does not identify which specific entity it is.

Entity linking requires a knowledge base containing the entities to which entity mentions can be linked. A popular choice for entity linking on open domain text are knowledge-bases based on Wikipedia, in which each page is regarded as a named entity. NED using Wikipedia entities has been also called wikification (see Wikify! an early entity linking system ). A knowledge base may also be induced automatically from training text or manually built.

Entity

An entity is something that exists in itself, actually or potentially, concretely or abstractly, physically or not. It need not be of material existence. In particular, abstractions and legal fictions are usually regarded as entities. In general, there is also no presumption that an entity is animate.

The word may refer, for example, to Bucephalus, the horse of Alexander; to a stone; to a cardinal number; to a language; or to ghosts or other spirits.

The word entitative is the adjective form of the noun entity. Something that is entitative is considered in its own right.

In philosophy, ontology is about the recognition of entities. The words ontic and entity are derived respectively from the ancient Greek and Latin present participles that mean 'being'.

Specialized uses

  • A DBMS entity is either a thing in the modeled world or a drawing element in an ERD.
  • In SUMO, Entity is the root node and stands for the universal class of individuals.
  • In VHDL, entity is the keyword for defining a new object.
  • Entity (disambiguation)

    Entity may refer to:

  • Entity, a unit
  • a part of an entity–relationship model or diagram
  • Character entity reference in HTML, XML or SGML
  • Entity (netlabel), a Belgian netlabel specialising in experimental electronic music
  • Entity class, in computer programming a class that represents a thing
  • In fiction

  • "Entity" (Stargate SG-1), an episode of the television series Stargate SG-1
  • The Entity, a 1981 horror film
  • Entity, a science fiction story by Poul Anderson and John Gergen
  • "The Entity" (South Park), an episode of the television series South Park
  • Ion (DC Comics), Parallax (comics), and five other related beings in DC comics, associated with the Lanterns Corps of the Blackest Night storyline. See White Lantern#Entity.
  • Entity (2012 film), a supernatural thriller film
  • Specialized uses

  • In a system, an entity refers to a component
  • A DBMS entity is either a thing in the modeled world or a drawing element in an ERD
  • In SUMO, Entity is the root node and stands for the universal class of individuals
  • Entity (album)

    Entity is the fifth studio album by technical death metal band Origin. It was released through Nuclear Blast, on June 7, 2011.It was released on CD & Vinyl.

    The album reached number 20 on the US Billboard Top New Artist Albums (Heatseekers).

    Reception

    Track listing

    All songs written and composed by Origin. 

    Personnel

  • Paul Ryan - guitar, vocals
  • Mike Flores - bass, vocals
  • John Longstreth - drums
  • Colin Marks - cover art, layout
  • Robert Rebeck - engineering, mixing
  • Colin Marston - mastering
  • References

  • http://columbia.jp/prod-info/COCB-60010/
  • Nuclear Blast Album Page accessed June 7, 2011
  • http://www.billboard.com/artist/317070/origin/chart
  • About.com review
  • Allmusic review
  • Blabbermouth.net review
  • MetalStorm review

  • Podcasts:

    • Training a custom ENTITY LINKING model with spaCy

      spaCy is an open-source library for advanced Natural Language Processing in Python. In this video, we show you how to create a custom Entity Linking model in spaCy to disambiguate different mentions of the person "Emerson" to unique identifiers in a knowledge base. We accomplish this by training a machine learning model on some text annotated from scratch with Prodigy. You can follow along in this notebook: https://github.com/explosion/projects/tree/master/nel-emerson/scripts/notebook_video.ipynb STEP BY STEP 00:00 – Introduction to the Entity Linking challenge 04:52 – Set up the knowledge base 10:30 – Annotate training data with Prodigy 19:19 – Parse the training data into the required format for spaCy 23:12 – Create and train the Entity Linking component 25:36 – Test the EL component o...

      published: 07 May 2020
    • Entity Linking

      In this video I motivate the problem of entity linking, summarize the existing approaches, and discuss some open challenges.

      published: 14 May 2021
    • Entity linking historical document OCR by combining Wikidata and Wikipedia

      Talk by Kai Labusch Berlin State Library, Germany Abstract: Named entities like persons, locations and organisations are a prominent target for search in digitized collections. While named entity recognition can be used to automatically detect named entities in texts, through the additional disambiguation and linking of the entities to authority files their usability for retrieval and linking to other sources is significantly improved. We used Wikidata to construct a comprehensive knowledge-base that holds information on linkable entities and combined it with a Wikipedia-derived corpus of text references that can be used by a neural network-based entity linking system to find references of entities in historical German texts. We demonstrate the feasibility of the approach on ~5,000,000 p...

      published: 20 Sep 2023
    • CoNEREL - Named Entity Recognition and Entity Linking

      CoNEREL is a system to extract named entities such as persons, locations, organizations and map them to the corresponding profiles in a knowledge base (Wikipedia). It is also a tool to study the relationships between entities mentioned in news articles and readers’ comments. Keywords: Named Entity Recognition (NER); Entity Linking (EL); Pair-Linking. Related work and publications: Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All. NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation.

      published: 27 Mar 2018
    • Wikification: How a computer can look up things it doesn't know

      This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://sites.google.com/umd.edu/2021cl1webpage/ Resources: * Non-neural Wikification https://www.aclweb.org/anthology/P11-1138/ * Neural Wikification https://www.aclweb.org/anthology/D17-1284/ * Wikification-powered QA http://users.umiacs.umd.edu/~jbg/docs/2020_www_delft.pdf (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review-and-rest

      published: 07 Apr 2021
    • Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)

      Entity linking functionality in spaCy: grounding textual mentions to knowledge base concepts (Sofie Van Landeghem, Explosion) ● Slides: https://drive.google.com/file/d/1EuGxcQLcXvjjkZ-KRUlwpr_doBVyEBEG/view ● spaCy IRL 2019: https://irl.spacy.io/2019 ● Sofie Van Landeghem on Twitter: https://twitter.com/OxyKodit SPACY IRL 2019 We were pleased to invite the spaCy community and other folks working on Natural Language Processing to Berlin this summer for a small and intimate event July 6, 2019. We booked a beautiful venue in one of Berlin's coolest neighborhoods, hand-picked an awesome lineup of speakers and scheduled plenty of social time to get to know each other and exchange ideas. CREDITS ● Jingle by Rachel Glassberg: https://rachelglassberg.com/music/ ● Illustrations by Josephine Rais:...

      published: 12 Jul 2019
    • Personal Entity, Concept, and Named Entity Linking in Conversations

      The CIKM 2022 presentation of the paper: "Personal Entity, Concept, and Named Entity Linking in Conversations" by Hideki Joko and Faegheh Hasibi Paper: https://arxiv.org/abs/2206.07836 Source code: https://github.com/informagi/conversational-entity-linking-2022 Abstract: Building conversational agents that can have natural and knowledge grounded interactions with humans requires understanding user utterances. Entity Linking (EL) is an effective technique for understanding natural language text and connecting it to external knowledge. It is, however, shown that the existing EL methods developed for annotating documents are suboptimal for conversations, where concepts and personal entities (e.g., “my cars”) are essential for understanding user utterances. In this paper, we introduce a co...

      published: 21 Feb 2023
    • Contextualized End-to-End Neural Entity Linking

      published: 21 Nov 2020
    • Tutorial on Entity Linking (Speaker: Laura Dietz)

      Abstract Entity Linking or Entity Disambiguation refers to the task of aligning unstructured text to collections of linked data. Given a text that mentions an entity, the task is to establish a link between any substring that refers to an entity to the entity's entry in the knowledge base. For instance, in a text about the American University of Beirut, we want to identify all of the substrings that refer to AUB and annotate them with links into the knowledge base. In this talk we use Wikipedia as our example knowledge base, but approaches are directly applicable to any other collection of Linked Data. A solution for Entity Linking is the key to extract more knowledge, such as facts and relations from text; Entity Linking provides the means to improve text retrieval (IR) and linked data ...

      published: 29 Mar 2014
    • Named Entity Linking (NEL): Connecting Entities to the World of Knowledge

      Named Entity Linking (NEL) (https://schneppat.com/named-entity-linking-nel.html) is a crucial task in Natural Language Processing (NLP) (https://schneppat.com/natural-language-processing-nlp.html) that aims to associate named entities mentioned in text with their corresponding entries in a knowledge base or reference database. By leveraging various techniques, NEL enables machines to bridge the gap between textual mentions and the rich information available in structured knowledge sources. This process enhances the understanding of textual data and facilitates numerous applications such as information retrieval, question answering systems, and knowledge graph construction. The Significance of NEL: In today's information-rich world, connecting named entities to a knowledge base provid...

      published: 30 Aug 2024
    developed with YouTube
    Training a custom ENTITY LINKING model with spaCy
    28:24

    Training a custom ENTITY LINKING model with spaCy

    • Order:
    • Duration: 28:24
    • Uploaded Date: 07 May 2020
    • views: 50801
    spaCy is an open-source library for advanced Natural Language Processing in Python. In this video, we show you how to create a custom Entity Linking model in spaCy to disambiguate different mentions of the person "Emerson" to unique identifiers in a knowledge base. We accomplish this by training a machine learning model on some text annotated from scratch with Prodigy. You can follow along in this notebook: https://github.com/explosion/projects/tree/master/nel-emerson/scripts/notebook_video.ipynb STEP BY STEP 00:00 – Introduction to the Entity Linking challenge 04:52 – Set up the knowledge base 10:30 – Annotate training data with Prodigy 19:19 – Parse the training data into the required format for spaCy 23:12 – Create and train the Entity Linking component 25:36 – Test the EL component on unseen data SPACY & PRODIGY ● spaCy: https://spacy.io ● Free online course: https://course.spacy.io ● Prodigy website & docs: https://prodi.gy THIS TUTORIAL ● Code & data: https://github.com/explosion/projects/tree/master/nel-emerson/ ● Entity linking in spaCy (spaCy IRL talk): https://www.youtube.com/watch?v=PW3RJM8tDGo&list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc&index=6 ● Entity linking docs: https://spacy.io/usage/training#entity-linker FOLLOW US ● Sofie Van Landeghem: https://twitter.com/OxyKodit ● Explosion: https://twitter.com/explosion_ai
    https://wn.com/Training_A_Custom_Entity_Linking_Model_With_Spacy
    Entity Linking
    33:37

    Entity Linking

    • Order:
    • Duration: 33:37
    • Uploaded Date: 14 May 2021
    • views: 1705
    In this video I motivate the problem of entity linking, summarize the existing approaches, and discuss some open challenges.
    https://wn.com/Entity_Linking
    Entity linking historical document OCR by combining Wikidata and Wikipedia
    33:00

    Entity linking historical document OCR by combining Wikidata and Wikipedia

    • Order:
    • Duration: 33:00
    • Uploaded Date: 20 Sep 2023
    • views: 184
    Talk by Kai Labusch Berlin State Library, Germany Abstract: Named entities like persons, locations and organisations are a prominent target for search in digitized collections. While named entity recognition can be used to automatically detect named entities in texts, through the additional disambiguation and linking of the entities to authority files their usability for retrieval and linking to other sources is significantly improved. We used Wikidata to construct a comprehensive knowledge-base that holds information on linkable entities and combined it with a Wikipedia-derived corpus of text references that can be used by a neural network-based entity linking system to find references of entities in historical German texts. We demonstrate the feasibility of the approach on ~5,000,000 pages of historical German texts obtained by OCR and show how the entity linking results can be used to group the entire historical text corpus by latent dirichlet allocation. All software components are also released as open source for others to adapt and reuse. SWIB23 Conference,11-13 September 2023, Berlin, Germany http://swib.org/swib23/ #swib23
    https://wn.com/Entity_Linking_Historical_Document_Ocr_By_Combining_Wikidata_And_Wikipedia
    CoNEREL - Named Entity Recognition and Entity Linking
    2:56

    CoNEREL - Named Entity Recognition and Entity Linking

    • Order:
    • Duration: 2:56
    • Uploaded Date: 27 Mar 2018
    • views: 1727
    CoNEREL is a system to extract named entities such as persons, locations, organizations and map them to the corresponding profiles in a knowledge base (Wikipedia). It is also a tool to study the relationships between entities mentioned in news articles and readers’ comments. Keywords: Named Entity Recognition (NER); Entity Linking (EL); Pair-Linking. Related work and publications: Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All. NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation.
    https://wn.com/Conerel_Named_Entity_Recognition_And_Entity_Linking
    Wikification: How a computer can look up things it doesn't know
    8:26

    Wikification: How a computer can look up things it doesn't know

    • Order:
    • Duration: 8:26
    • Uploaded Date: 07 Apr 2021
    • views: 945
    This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://sites.google.com/umd.edu/2021cl1webpage/ Resources: * Non-neural Wikification https://www.aclweb.org/anthology/P11-1138/ * Neural Wikification https://www.aclweb.org/anthology/D17-1284/ * Wikification-powered QA http://users.umiacs.umd.edu/~jbg/docs/2020_www_delft.pdf (Including homeworks and reading.) Music: https://soundcloud.com/alvin-grissom-ii/review-and-rest
    https://wn.com/Wikification_How_A_Computer_Can_Look_Up_Things_It_Doesn't_Know
    Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)
    20:08

    Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)

    • Order:
    • Duration: 20:08
    • Uploaded Date: 12 Jul 2019
    • views: 10860
    Entity linking functionality in spaCy: grounding textual mentions to knowledge base concepts (Sofie Van Landeghem, Explosion) ● Slides: https://drive.google.com/file/d/1EuGxcQLcXvjjkZ-KRUlwpr_doBVyEBEG/view ● spaCy IRL 2019: https://irl.spacy.io/2019 ● Sofie Van Landeghem on Twitter: https://twitter.com/OxyKodit SPACY IRL 2019 We were pleased to invite the spaCy community and other folks working on Natural Language Processing to Berlin this summer for a small and intimate event July 6, 2019. We booked a beautiful venue in one of Berlin's coolest neighborhoods, hand-picked an awesome lineup of speakers and scheduled plenty of social time to get to know each other and exchange ideas. CREDITS ● Jingle by Rachel Glassberg: https://rachelglassberg.com/music/ ● Illustrations by Josephine Rais: https://josephinerais.com
    https://wn.com/Sofie_Van_Landeghem_Entity_Linking_Functionality_In_Spacy_(Spacy_Irl_2019)
    Personal Entity, Concept, and Named Entity Linking in Conversations
    10:07

    Personal Entity, Concept, and Named Entity Linking in Conversations

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    • Duration: 10:07
    • Uploaded Date: 21 Feb 2023
    • views: 73
    The CIKM 2022 presentation of the paper: "Personal Entity, Concept, and Named Entity Linking in Conversations" by Hideki Joko and Faegheh Hasibi Paper: https://arxiv.org/abs/2206.07836 Source code: https://github.com/informagi/conversational-entity-linking-2022 Abstract: Building conversational agents that can have natural and knowledge grounded interactions with humans requires understanding user utterances. Entity Linking (EL) is an effective technique for understanding natural language text and connecting it to external knowledge. It is, however, shown that the existing EL methods developed for annotating documents are suboptimal for conversations, where concepts and personal entities (e.g., “my cars”) are essential for understanding user utterances. In this paper, we introduce a collection and a tool for entity linking in conversations. We provide EL annotations for 1,327 conversational utterances, consisting of links to named entities, concepts, and personal entities. The dataset is used for training our toolkit for conversational entity linking, CREL. Unlike existing EL methods, CREL is developed to identify both named entities and concepts. It also utilizes coreference resolution techniques to identify personal entities and their references to the explicit entity mentions in the conversations. We compare CREL with state-of-the-art techniques and show that it outperforms all existing baselines.
    https://wn.com/Personal_Entity,_Concept,_And_Named_Entity_Linking_In_Conversations
    Contextualized End-to-End Neural Entity Linking
    9:52

    Contextualized End-to-End Neural Entity Linking

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    • Duration: 9:52
    • Uploaded Date: 21 Nov 2020
    • views: 431
    https://wn.com/Contextualized_End_To_End_Neural_Entity_Linking
    Tutorial on Entity Linking (Speaker: Laura Dietz)
    39:38

    Tutorial on Entity Linking (Speaker: Laura Dietz)

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    • Duration: 39:38
    • Uploaded Date: 29 Mar 2014
    • views: 6195
    Abstract Entity Linking or Entity Disambiguation refers to the task of aligning unstructured text to collections of linked data. Given a text that mentions an entity, the task is to establish a link between any substring that refers to an entity to the entity's entry in the knowledge base. For instance, in a text about the American University of Beirut, we want to identify all of the substrings that refer to AUB and annotate them with links into the knowledge base. In this talk we use Wikipedia as our example knowledge base, but approaches are directly applicable to any other collection of Linked Data. A solution for Entity Linking is the key to extract more knowledge, such as facts and relations from text; Entity Linking provides the means to improve text retrieval (IR) and linked data retrieval. In this talk I am going to cover several approaches to Entity Linking from simple to complex and discuss advantages and shortcomings. Different approaches will make use of natural language processing, information retrieval, machine learning and graphical probabilistic models. Bio Dr. Laura Dietz is a post-doctoral researcher working with Bruce Croft at the Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts. Before that she was working with Andrew McCallum at University of Massachusetts. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatik in early 2011, being supervised by Tobias Scheffer and Gerhard Weikum.
    https://wn.com/Tutorial_On_Entity_Linking_(Speaker_Laura_Dietz)
    Named Entity Linking (NEL): Connecting Entities to the World of Knowledge
    12:19

    Named Entity Linking (NEL): Connecting Entities to the World of Knowledge

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    • Duration: 12:19
    • Uploaded Date: 30 Aug 2024
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    Named Entity Linking (NEL) (https://schneppat.com/named-entity-linking-nel.html) is a crucial task in Natural Language Processing (NLP) (https://schneppat.com/natural-language-processing-nlp.html) that aims to associate named entities mentioned in text with their corresponding entries in a knowledge base or reference database. By leveraging various techniques, NEL enables machines to bridge the gap between textual mentions and the rich information available in structured knowledge sources. This process enhances the understanding of textual data and facilitates numerous applications such as information retrieval, question answering systems, and knowledge graph construction. The Significance of NEL: In today's information-rich world, connecting named entities to a knowledge base provides a deeper level of context and enables more comprehensive analysis. NEL enables systems to access additional information related to entities, such as their attributes, relationships, and semantic connections, thus enhancing the quality and richness of the extracted information. Challenges in NEL: Named Entity Linking poses several challenges due to the complexities of language, entity ambiguity, and the vastness of knowledge bases. Some key challenges include: 1. Entity Disambiguation: Identifying the correct entity when an entity mention is ambiguous or has multiple possible interpretations. Resolving these ambiguities requires contextual understanding and leveraging various clues within the text. 2. Knowledge Base Coverage: Knowledge bases may not encompass all entities mentioned in text, especially for emerging or domain-specific entities. Handling out-of-vocabulary or rare entities becomes a challenge in NEL. 3. Named Entity Variation: Entities can have different forms, such as acronyms, abbreviations, or alternative names. Linking these variations to the corresponding entity in the knowledge base requires robust techniques that can handle such variability. Approaches to NEL: NEL techniques employ a combination of linguistic analysis, machine learning (https://schneppat.com/machine-learning-ml.html) , and information retrieval strategies. These approaches leverage entity recognition (https://schneppat.com/named-entity-recognition-ner.html) and disambiguation algorithms to determine the context and semantic meaning of named entities. Conclusion: Named Entity Linking is a vital component in unlocking the potential of textual data by connecting named entities to the world of knowledge. Overcoming challenges in entity disambiguation, knowledge base coverage, and named entity variation is crucial for accurate and robust NEL. As NEL techniques advance, we can expect improved systems that seamlessly link entities to knowledge bases, paving the way for enhanced information extraction, knowledge management, and intelligent applications in diverse domains. Kind regards by Schneppat AI (https://schneppat.com)
    https://wn.com/Named_Entity_Linking_(Nel)_Connecting_Entities_To_The_World_Of_Knowledge
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    Training a custom ENTITY LINKING model with spaCy

    spaCy is an open-source library for advanced Natural Language Processing in Python. In this video, we show you how to create a custom Entity Linking model in spaCy to disambiguate different mentions of the person "Emerson" to unique identifiers in a knowledge base. We accomplish this by training a machine learning model on some text annotated from scratch with Prodigy. You can follow along in this notebook: https://github.com/explosion/projects/tree/master/nel-emerson/scripts/notebook_video.ipynb STEP BY STEP 00:00 – Introduction to the Entity Linking challenge 04:52 – Set up the knowledge base 10:30 – Annotate training data with Prodigy 19:19 – Parse the training data into the required format for spaCy 23:12 – Create and train the Entity Linking component 25:36 – Test the EL component on unseen data SPACY & PRODIGY ● spaCy: https://spacy.io ● Free online course: https://course.spacy.io ● Prodigy website & docs: https://prodi.gy THIS TUTORIAL ● Code & data: https://github.com/explosion/projects/tree/master/nel-emerson/ ● Entity linking in spaCy (spaCy IRL talk): https://www.youtube.com/watch?v=PW3RJM8tDGo&list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc&index=6 ● Entity linking docs: https://spacy.io/usage/training#entity-linker FOLLOW US ● Sofie Van Landeghem: https://twitter.com/OxyKodit ● Explosion: https://twitter.com/explosion_ai
    28:24
    Training a custom ENTITY LINKING model with spaCy
    spaCy is an open-source library for advanced Natural Language Processing in Python. In thi...
    published: 07 May 2020
    Play in Full Screen
    33:37
    Entity Linking
    In this video I motivate the problem of entity linking, summarize the existing approaches,...
    published: 14 May 2021
    Play in Full Screen
    33:00
    Entity linking historical document OCR by combining Wikidata and Wikipedia
    Talk by Kai Labusch Berlin State Library, Germany Abstract: Named entities like persons, ...
    published: 20 Sep 2023
    Play in Full Screen
    2:56
    CoNEREL - Named Entity Recognition and Entity Linking
    CoNEREL is a system to extract named entities such as persons, locations, organizations an...
    published: 27 Mar 2018
    Play in Full Screen
    8:26
    Wikification: How a computer can look up things it doesn't know
    This is a single lecture from a course. If you you like the material and want more contex...
    published: 07 Apr 2021
    Play in Full Screen
    20:08
    Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)
    Entity linking functionality in spaCy: grounding textual mentions to knowledge base concep...
    published: 12 Jul 2019
    Play in Full Screen
    10:07
    Personal Entity, Concept, and Named Entity Linking in Conversations
    The CIKM 2022 presentation of the paper: "Personal Entity, Concept, and Named Entity Link...
    published: 21 Feb 2023
    Play in Full Screen
    9:52
    Contextualized End-to-End Neural Entity Linking
    published: 21 Nov 2020
    Play in Full Screen
    39:38
    Tutorial on Entity Linking (Speaker: Laura Dietz)
    Abstract Entity Linking or Entity Disambiguation refers to the task of aligning unstructu...
    published: 29 Mar 2014
    Play in Full Screen
    12:19
    Named Entity Linking (NEL): Connecting Entities to the World of Knowledge
    Named Entity Linking (NEL) (https://schneppat.com/named-entity-linking-nel.html) is a cru...
    published: 30 Aug 2024
    Play in Full Screen

    Entity linking

    In natural language processing, entity linking, named entity disambiguation (NED), named entity recognition and disambiguation (NERD) or named entity normalization (NEN) is the task of determining the identity of entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred as "Paris". NED is different from named entity recognition (NER) in that NER identifies the occurrence or mention of a named entity in text but it does not identify which specific entity it is.

    Entity linking requires a knowledge base containing the entities to which entity mentions can be linked. A popular choice for entity linking on open domain text are knowledge-bases based on Wikipedia, in which each page is regarded as a named entity. NED using Wikipedia entities has been also called wikification (see Wikify! an early entity linking system ). A knowledge base may also be induced automatically from training text or manually built.

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