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23 results on '"MANCHANDA, PIKAKSHI"'

2. WebParF: A Web partitioning framework for Parallel Crawlers

Author

Gupta, Sonali, Bhatia, Komal kumar, and Manchanda, Pikakshi

Subjects
Computer Science - Information Retrieval
Abstract

With the ever proliferating size and scale of the WWW [1] efficient ways of exploring content are of increasing importance. How can we efficiently retrieve information from it through crawling? And in this era of tera and multi-core processors, we ought to think of multi-threaded processes as a serving solution. So, even better how can we improve the crawling performance by using parallel crawlers that work independently? The paper devotes to the fundamental development in the field of parallel crawlers [4] highlighting the advantages and challenges arising from its design. The paper also focuses on the aspect of URL distribution among the various parallel crawling processes or threads and ordering the URLs within each distributed set of URLs. How to distribute URLs from the URL frontier to the various concurrently executing crawling process threads is an orthogonal problem. The paper provides a solution to the problem by designing a framework WebParF that partitions the URL frontier into a several URL queues while considering the various design issues., Comment: 8pages, 7 figures, ISSN : 0975-3397 Vol.5 no.8, 2013

Published
2014

5. Adapting Named Entity Types to New Ontologies in a Microblogging Environment

Author

Fersini, Elisabetta, Manchanda, Pikakshi, Messina, Enza, Nozza, Debora, Palmonari, Matteo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Mouhoub, Malek, editor, Sadaoui, Samira, editor, Ait Mohamed, Otmane, editor, and Ali, Moonis, editor

Published
2018
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6. Utilizing Microblog Data in a Topic Modelling Framework for Scientific Articles’ Recommendation

Author

Younus, Arjumand, Qureshi, Muhammad Atif, Manchanda, Pikakshi, O’Riordan, Colm, Pasi, Gabriella, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Aiello, Luca Maria, editor, and McFarland, Daniel, editor

Published
2014
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8. Artificial Intelligence

Author

Börner, Wolfgang, Rohland, Hendrik, Kral-Börner, Christina, Karner, Lina, Liarokapis, Fotis, Kuroczynski, Piotr, Görz, Günther, Schlieder, Christoph, Bartlett, F. Michael, Turkel, William J., Noback, Andreas, Grobe, Lars Oliver, Dorn, Amelie, Rocha Souza, Renato, Koch, Gerda, Methuku, Japesh, Abgaz, Yalemisew, Myridis, Nikolaos, Sarakatsianou, Dimitra, Tintner, Johannes, Spangl, Bernhard, Melcher, Michael, Kazimi, Bashir, Malek, Katharina, Thiemann, Frank, Sester, Monika, Sarris, Apostolos, Küçükdemirci, Melda, Kalayci, Tuna, Verschoof-Van Der Vaart, Wouter, Landauer, Juergen, Wolf, Julien, Pope-Carter, Finnegan, Johnson, Paul S., Yurchak, Igor, Yurchak, Natalie, Sahaydak, Mykhaylo, Rutkovska, Olga, Biletskyy, Vitaliy, Pfaffenbichler, Franz Xaver, Eysn, Lothar, Lehner, Hubert, Kordasch, Sara Lena, Hartmann, Gerhard, Herzog, Irmela, Bibby, David, Block-Berlitz, Marco, Oczipka, Martin, Bommhardt-Richter, Michael, Brüll, Vanessa, Dorninger, Peter, Studnicka, Nikolaus, Enderli, Livia, Villa, Daniele, Cecco, Lorenzo, Lengyel, Dominik, Toulouse, Catherine, Polig, Martina, Schenkel, Arnaud, Zhang, Zheng, Debeir, Olivier, Parsons, Stephen, Gessel, Kristina, Parker, Clifford, Seales, William, Monamy, Elisabeth, Peter, Sigrid, Frampton, Claire, Barandoni, Cristiana, Giulierini, Paolo, Zamparo, Luca, Faresin, Emanuela, Zilio, Daniel, Bauer, Peter, Kaufmann, Viktor, Sulzer, Wolfgang, Lienhart, Werner, Mikl, Thomas, Seier, Gernot, Somigli, Lapo, Palla, Arianna, Toso, Francesca, Emilio, Giulia, Verdiani, Giorgio, Della Monaca, Gualtiero, Smart, Andi, Mosconi, Cristina, Manchanda, Pikakshi, Gonzales, Paloma, Nagakura, Takehiko, Silvestru, Claudiu, Aryankhesal, Fred Farshid, Danthine, Brigit, Hiebel, Gerald, Lehar, Philipp, Stadler, Harald, Pasquali, Andrea, Giraudeau, Stéphane, Capparelli, Francesco, Galatolo, Olimpia, Cecconi, Eleonora, Perera, Walpola Layantha, Messemer, Heike, Heinz, Matthias, Kretzschmar, Michael, Bruderer, Oliver, Toleva-Nowak, Lena, Anafi, Babatunde, Hyvönen, Eero, Koho, Mikko, Cortella, Laurent, Bertrand, Loïc, Stols-Witlox, Maartje, Mihaljevic, Branka, Ferreira, Luis M., Casimiro, M. Helena, Corregidor, Victoria, Joosten, Ineke, Vasquez S., Pablo A., Marusic, Katarina, Alves, Luís C., Simon, Aliz, Han, Bumsoo, Horak, Celina I., and Wimberger, Sindre

Published
2022
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9. Monumental Computations

Author

Museen Der Stadt Wien, Stadtarchäologie, Gawronski, Jerzy, Carver, Jay, Degraeve, Ann, Stead, Stephen, Rauxloh, Peter, Herzog, Irmela, Weber, Claus, Richards, Julian, Evans, Tim, Green, Katie, Niven, Kieron, Block-Berlitz, Marco, Oczipka, Martin, Do Duc, Huy, Rohland, Hendrik, Franken, Christina, Batbayar, Tumurochir, Erdenebat, Ulambayar, Kampouris, Apostolos C., Giannoulis, Dimitros V., Androulaki, Maria, Vidalis, Georgios, Inglezakis, Ioannis-Georgios, Chatzidakis, Georgios, Maravelaki, Pagona, Parthenios, Panagiotis, Reinfeld, Michaela, Fritsch, Bernhard, Cichocki, Otto, Groiss, Bernhard, Wallner, Mario, Weissl, Michael, Linzen, Sven, Reichert, Susanne, Bemmann, Jan, Stolz, Ronny, Sharifi, Reza, Ibrahimi, Alireza, Hartnell, Tobin, Razmahang, Yalda, Dler, Mohammed, Azad Tawfeeq, Adam, Crabb, Nicholas, Carey, Chris, Howard, Andy, Jackson, Robin, Komp, Rainer, Goldmann, Lukas, Montanaro, Rosanna, Mosconi, Cristina, Smart, Andi, Nevola, Fabrizio, Cruz, Tiago, Grellert, Marc, Wölfel, Norwina, Ristow, Sebastian, Özcan, Ertan, Wiehen, Michael, Guillaume, Henry-Louis, Schenkel, Arnaud, Hanussek, Benjamin, Horňák, Milan, Hrnčiarik, Erik, Minaroviech, Jana, Doneus, Michael, Shinoto, Maria, Hajima, Hideyuki, Nakamura, Naoko, Kazimi, Bashir, Malek, Katharina, Thiemann, Frank, Sester, Monika, Due, Øivind, Løseth, Kristian, Meyer-Heß, M. Fabian, Pfeffer, Ingo, Jürgens, Carsten, Morrison, Wendy, Peveler, Edward, Somrak, Maja, Kokalj, Žiga, Džeroski, Sašo, Kuroczynski, Piotr, Silvestru, Claudiu, Mori, Naoki, Almahari, Salman, Higo, Tokihisa, Suemori, Kaoru, Suita, Hiroshi, Yasumuro, Yoshihiro, Zarogianni, Eleni, Siountri, Konstantina, Michailidis, Neoptolemos, Vergados, Dimitrios D., Polig, Martina, Reiter, Franzsika, Tintner, Johannes, Spangl, Bernhard, Smidt, Ena, Grabner, Michael, Ridderhof, Benno, Verdiani, Giorgio, Börner, Wolfgang, Soeters, Gilbert, Vital, Rebeka, Papadopoulos, Costas, Moullou, Dorina, Doulos, Lambros, Luego, Pedro, Grobe, Lars O., Noback, Andreas, Lang, Franziska, Schintlmeister, Luise, Schwaiger, Helmut, Iliades, Ioannis, Iliadis, Georgios, Chistaras, Vlassis, Luengo, Pedro, Debeir, Olivier, Lanen, Rowin Van, Kosian, Menne, Abrahamse, Jaap Evert, Antlej, Kaja, Rebernik, Nataša, Jaklič, Lailan, Solina, Franc, Cartledge, Kayla, Erič, Miran, Nagakura, Takehiko, Mann, Eytan, Keller, Eliyahu, Jarzombek, Mark, Barandoni, Christiana, Yeke, Oğuz, Cerri, Giada, Magnelli, Adele, Destile, Aurelio, Pantile, Davide, Trimani, Valentina, Vergani, Filippo, Zaffi, Leonardo, Viti, Stefania, Ciuffreda, Anna Livia, Coli, Massimo, Micheloni, Michelangelo, Monamy, Elisabeth, Peter, Sigrid, Arnold, Bernhard, Frampton, Claire, Weberstorfer, Miriam, Kaspar, Emanuel, Manchanda, Pikakshi, Kiran, Ali S., Kaplan, Celal, Wohlers, Lars, Aspöck, Edeltraud, Geser, Guntram, Hiebel, Gerald, Trognitz, Martina, Felicetti, Achille, Galluccio, Ilenia, Danthine, Brigit, Goldenberg, Gert, Grutsch, Caroline, Hanke, Klaus, Staudt, Markus, Scherer-Windisch, Manuel, Clados, Christiane, Messemer, Heike, Bruderer, Oliver, Lengyel, Dominik, Toulouse, Catherine, Mann, Katharina Ute, Bibby, David, Blesl, Christoph, Göldner, Reiner, Gieser, Simon, Wolters, Katrin, High-Steskal, Nicole, Rembart, Laura, Schubert, Christof, El-Marjaoui, Houssam, Ait Lyazidi, Saadia, Haddad, Mustapha, Lamhasni, Taibi, Benyaich, Fouad, Ben-Ncer, Abdelouahed, Bonazza, Alessandra, Bommhardt-Richter, Michael, Anzani, Giovanni, Galatolo, Olimpia, Algostino, Francesco, Cecconi, Eleonora, Bochmann, Hilmar, Caldararo, Annalina, Dare, Peter, Papaioannou, Maria, Koutellas, Mihalis, Enderli, Livia, Ma, Lijun, Lu, Xiaobo, Maggi, Sara, Maramai, Ambra, Marras, Silvia, Bracalenti, Federica, Pollinzi, Fabiola, and Pobežin, Gregor

Published
2021
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12. EVALITA. Evaluation of NLP and Speech Tools for Italian

Author

Alzetta, Chiara, Attardi, Giuseppe, Badino, Leonardo, Barbieri, Francesco, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Bentham, Jereemi, Bhardwaj, Divyanshu, Bolioli, Andrea, Bondielli, Alessandro, Bosco, Cristina, Buscaldi, Davide, Caputo, Annalina, Caselli, Tommaso, Castellucci, Giuseppe, Cecchini, Flavio Massimiliano, Cieliebak, Mark, Cimino, Andrea, Corcoglioniti, Francesco, Cosi, Piero, Covella, Vito Vincenzo, Cozza, Vittoria, Croce, Danilo, Cutugno, Franco, Daelemans, Walter, Dell’Orletta, Felice, Deriu, Jan, De Carolis, Berardina, de Gemmis, Marco, Di Noia, Tommaso, Di Rosa, Emanuele, Durante, Alberto, Feltracco, Anna, Ferilli, Stefano, Fersini, Elisabetta, Fonseca, Erick R., Frenda, Simona, Gelbukh, Alexander, Gentile, Anna Lisa, Giuliano, Claudio, Graff, Mario, Hernandez-Farias, Delia Irazù, Horsmann, Tobias, La Bruna, Wanda, Lenci, Alessandro, Lops, Pasquale, Lovecchio, Francesco, Magnini, Bernardo, Magnolini, Simone, Majumder, Goutam, Manchanda, Pikakshi, Manzari, Vito, Mazzei, Alessandro, Messina, Enza, Minard, Anne-Lyse, Miranda-Jiménez, Sabino, Moctezuma, Daniela, Monachini, Monica, Nechaev, Yaroslav, Nissim, Malvina, Novielli, Nicole, Nozza, Debora, Paci, Giulio, Pakray, Partha, Palmero Aprosio, Alessio, Palmonari, Matteo, Passaro, Lucia C., Patti, Viviana, Pipitone, Arianna, Pirrone, Roberto, Plank, Barbara, Qwaider, Mohammed R. H., Redavid, Domenico, Rizzo, Giuseppe, Russo, Irene, Saha, Saurav, Sartiano, Daniele, Sas, Cezar, Semplici, Federica, Simi, Maria, Speranza, Manuela, Sprugnoli, Rachele, Stemle, Egon W., Sucameli, Irene, Tamburini, Fabio, Tellez, Eric S., Tirone, Giuseppe, Zesch, Torsten, Basile, Pierpaolo, Cutugno, Franco, Nissim, Malvina, Patti, Viviana, and Sprugnoli, Rachele

Subjects
reconnaissance téléphonique articulatoire, entité appelée rEcognition et liens dans le tweets italien, sentiment polarity classification, event factuality annotation, articulatory phone recognition, Linguistics, CF, annotazione fattualità degli eventi, etichettare per messaggi social media, riconoscimento telefonico articolare, classificazione polarità sentimenti, tagging for italian social media texts, computational linguistics, named entity rEcognition and linking in italian tweets, linguistique computationelle, classement polarité sentiments, LAN009000, entità chiamata rEcognition e collegamenti nei tweet italiani, linguistica computazionale, étiqueter les messages des médias sociaux, annotation de facturation de l'événement
Abstract

EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for the Italian language: since 2007 shared tasks have been proposed covering the analysis of both written and spoken language with the aim of enhancing the development and dissemination of resources and technologies for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it/) and it is supported by the NLP Special Interest Group of the Italian Association for Artificial Intelligence (AI*IA, http://www.aixia.it/) and by the Italian Association of Speech Science (AISV, http://www.aisv.it/). In this volume, we collect the reports of the tasks’ organisers and of the participants to all of the EVALITA 2016’s tasks, which are the following: ArtiPhone - Articulatory Phone Recognition; FactA - Event Factuality Annotation; NEEL-IT - Named Entity rEcognition and Linking in Italian Tweets; PoSTWITA - POS tagging for Italian Social Media Texts; QA4FAQ - Question Answering for Frequently Asked Questions; SENTIPOLC - SENTIment POLarity Classification. Notice that the volume does not include reports related to the IBM Watson Services Challenge organised by IBM Italy, but information can be found at http://www.evalita.it/2016/tasks/ibm-challenge. Before the task and participant reports, we also include an overview to the campaign that describes the tasks in more detail, provides figures on the participants, and, especially, highlights the innovations introduced at this year’s edition. An additional report presents a reflection on the outcome of two questionnaires filled by past participants and organisers of EVALITA, and of the panel “Raising Interest and Collecting Suggestions on the EVALITA Evaluation Campaign” held at CLIC-it 2015.

Published
2017

13. Towards Adaptation of Named Entity Recognition and Linking Frameworks

Author

MANCHANDA, PIKAKSHI, Manchanda, P, FERSINI, ELISABETTA, and MAURI, GIANCARLO

Subjects
Recognition, Ontology, Entity, Twitter, Linking, INF/01 - INFORMATICA
Abstract

L'estrazione di informazioni strutturate a partire dal “web non strutturato”, ha suscitato un notevole interesse da parte delle comunità scientifiche che si occupano di elaborazione del linguaggio naturale e di sistemi basati sulla conoscenza per sviluppare a pieno la visione del “web semantico”. Nell'era moderna, l'uso pervasivo e diffuso delle reti sociali ha portato alla produzione di un flusso continuo di informazioni su piattaforme quali Twitter o Facebook, definite anche piattaforme di microblogging. Tali sorgenti informative, accessibili in tempo reale, producono informazioni caratterizzate dalla presenza costante di rumore e ambiguità linguistiche che rendono particolarmente difficoltoso il compito di estrarre informazioni strutturate. Tale estrazione è tuttavia cruciale per poter arricchire grandi basi di conoscenza, oggi usate in molte applicazioni industriali e di ricerca, con informazioni nuove e rilevanti. Ne risulta che nell'ultimo decennio sono aumentati significativamente gli sforzi della ricerca nel campo dell’elaborazione del linguaggio naturale per l'estrazione di informazioni da piattaforme di microblogging, con particolare attenzione nei confronti dell’estrazione e identificazione di entità nominali (anche Named Entity Extraction and Linking o NEEL). Oggigiorno esistono numerosi sistemi di NEEL, di cui la maggior parte però creati a scopo commerciale. La calibrazione dei componenti di un sistema di NEEL, cioè dei componenti per la rilevazione, la disambiguazione e l'identificazione di entità nominali, nel caso di piattaforme di microblogging come Twitter e Facebook è difficile in particolare a causa delle tipologie di testo considerato. Mancano approcci di ricerca sistematici volti a guidare l'utilizzo e il miglioramento di tali componenti, per la realizzazione di sistemi più robusti, in grado di meglio adattarsi all’emergere di nuove informazioni, e nuovi interessi (ad esempio, a estrarre tipi di entità nuovi rispetto a quelli considerati in passato). La presente tesi discute le sfide affrontate dai sistemi tradizionali di NEEL qualora questi si misurino con formati di testo quali i tweet, ed esplora vari approcci per migliorare le prestazioni dei singoli componenti e di un sistema di NEEL nel suo insieme. L'ipotesi chiave del presente lavoro di tesi è che sia possibile costruire sistemi robusti usando dove possibile, componenti esistenti, e che la prestazione di un sistema nel suo complesso possa essere migliorata qualora si sviluppino meccanismi di feedback atti a fare si che alcuni componenti vengano usati per migliorare le prestazioni di altri componenti. A tale scopo, in questa tesi sono state indagate tecniche supervisionate e non supervisionate che si sono rivelate efficaci per aumentare l'accuratezza di un sistema nel suo complesso mediante meccanismi di feedback e di adattamento a nuovi domini, per formati di testo ambigui e rumorosi provenienti dalla piattaforma di microblogging Twitter. Natural Language Processing and Knowledge Base Experts are actively involved in extracting structured information from the Unstructured Web in order to realize the Semantic Web Vision. Diverse forms of unstructured information is easily available today to research scientists from social media platforms such as Twitter and Facebook in real time. %Knowledge discovery from the diverse forms of unstructured information available today from social media platforms such as Twitter and Facebook in real time, thus, plays a key role for this goal. The comprehensive and widespread use of such platforms in the modern age has led to a continuous stream of evolving information along with a constant presence of noise, and ambiguity which makes the task of extracting structured information difficult. An essential step is therefore identification of relevant information from the point of view of knowledge base enrichment. As a result, research efforts towards Information Extraction and Natural Language Processing Frameworks have increased significantly over the past decade, Named Entity Extraction and Linking (NEEL) Frameworks being one of the very prevalent ones. Numerous NEEL frameworks exist today, however, mostly for commercial purposes. The orchestration of components of a NEEL framework, i.e., named entity recognition component, named entity disambiguation and named entity linking component, for microblogging platforms such as Twitter and Facebook is difficult in particular due to the type of text under consideration. As a result, there is little research in the use and improvement of such components towards a more robust framework that can be adapted to emerging information in real time. This thesis discusses the challenges faced by conventional NEEL frameworks when faced with textual formats such as tweets and investigates several approaches to improve the performance of the components and of the NEEL framework as a whole. A key hypothesis is that the performance of such a framework depreciates when dealing with social media platforms, and if one component can be used to improve the performance of the other, the overall performance can be improved as well. Supervised and unsupervised techniques have been investigated in this thesis to this end, which prove to be effective in increasing the overall accuracy of the framework when faced with noisy and ambiguous textual formats from the microblogging platform of Twitter.

Published
2017

14. Towards Adaptation of Named Entity Recognition and Linking Frameworks

Author

PALMONARI, MATTEO LUIGI, Manchanda, P, FERSINI, ELISABETTA, MAURI, GIANCARLO, MANCHANDA, PIKAKSHI, PALMONARI, MATTEO LUIGI, Manchanda, P, FERSINI, ELISABETTA, MAURI, GIANCARLO, and MANCHANDA, PIKAKSHI

Abstract

L'estrazione di informazioni strutturate a partire dal “web non strutturato”, ha suscitato un notevole interesse da parte delle comunità scientifiche che si occupano di elaborazione del linguaggio naturale e di sistemi basati sulla conoscenza per sviluppare a pieno la visione del “web semantico”. Nell'era moderna, l'uso pervasivo e diffuso delle reti sociali ha portato alla produzione di un flusso continuo di informazioni su piattaforme quali Twitter o Facebook, definite anche piattaforme di microblogging. Tali sorgenti informative, accessibili in tempo reale, producono informazioni caratterizzate dalla presenza costante di rumore e ambiguità linguistiche che rendono particolarmente difficoltoso il compito di estrarre informazioni strutturate. Tale estrazione è tuttavia cruciale per poter arricchire grandi basi di conoscenza, oggi usate in molte applicazioni industriali e di ricerca, con informazioni nuove e rilevanti. Ne risulta che nell'ultimo decennio sono aumentati significativamente gli sforzi della ricerca nel campo dell’elaborazione del linguaggio naturale per l'estrazione di informazioni da piattaforme di microblogging, con particolare attenzione nei confronti dell’estrazione e identificazione di entità nominali (anche Named Entity Extraction and Linking o NEEL). Oggigiorno esistono numerosi sistemi di NEEL, di cui la maggior parte però creati a scopo commerciale. La calibrazione dei componenti di un sistema di NEEL, cioè dei componenti per la rilevazione, la disambiguazione e l'identificazione di entità nominali, nel caso di piattaforme di microblogging come Twitter e Facebook è difficile in particolare a causa delle tipologie di testo considerato. Mancano approcci di ricerca sistematici volti a guidare l'utilizzo e il miglioramento di tali componenti, per la realizzazione di sistemi più robusti, in grado di meglio adattarsi all’emergere di nuove informazioni, e nuovi interessi (ad esempio, a estrarre tipi di entità nuovi rispetto a quelli conside, Natural Language Processing and Knowledge Base Experts are actively involved in extracting structured information from the Unstructured Web in order to realize the Semantic Web Vision. Diverse forms of unstructured information is easily available today to research scientists from social media platforms such as Twitter and Facebook in real time. %Knowledge discovery from the diverse forms of unstructured information available today from social media platforms such as Twitter and Facebook in real time, thus, plays a key role for this goal. The comprehensive and widespread use of such platforms in the modern age has led to a continuous stream of evolving information along with a constant presence of noise, and ambiguity which makes the task of extracting structured information difficult. An essential step is therefore identification of relevant information from the point of view of knowledge base enrichment. As a result, research efforts towards Information Extraction and Natural Language Processing Frameworks have increased significantly over the past decade, Named Entity Extraction and Linking (NEEL) Frameworks being one of the very prevalent ones. Numerous NEEL frameworks exist today, however, mostly for commercial purposes. The orchestration of components of a NEEL framework, i.e., named entity recognition component, named entity disambiguation and named entity linking component, for microblogging platforms such as Twitter and Facebook is difficult in particular due to the type of text under consideration. As a result, there is little research in the use and improvement of such components towards a more robust framework that can be adapted to emerging information in real time. This thesis discusses the challenges faced by conventional NEEL frameworks when faced with textual formats such as tweets and investigates several approaches to improve the performance of the components and of the NEEL framework as a whole. A key hypothesis is that the performance of such a f

Published
2017

15. TWINE: A real-time system for TWeet analysis via INformation extraction

Author

Nozza, D, Ristagno, F, Palmonari, M, Fersini, E, Manchanda, P, Messina, V, NOZZA, DEBORA, PALMONARI, MATTEO LUIGI, FERSINI, ELISABETTA, MANCHANDA, PIKAKSHI, MESSINA, VINCENZINA, Nozza, D, Ristagno, F, Palmonari, M, Fersini, E, Manchanda, P, Messina, V, NOZZA, DEBORA, PALMONARI, MATTEO LUIGI, FERSINI, ELISABETTA, MANCHANDA, PIKAKSHI, and MESSINA, VINCENZINA

Abstract

In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at http://twinemind. cloudapp.net/streaming1,2.

Published
2017

16. Towards adaptation of Named Entity classification

Author

Manchanda, P, Fersini, E, Palmonari, M, Nozza, D, Messina, V, MANCHANDA, PIKAKSHI, FERSINI, ELISABETTA, PALMONARI, MATTEO LUIGI, NOZZA, DEBORA, MESSINA, VINCENZINA, Manchanda, P, Fersini, E, Palmonari, M, Nozza, D, Messina, V, MANCHANDA, PIKAKSHI, FERSINI, ELISABETTA, PALMONARI, MATTEO LUIGI, NOZZA, DEBORA, and MESSINA, VINCENZINA

Abstract

Numerous state-of-the-art Named Entity Recognition (NER) systems use different classification schemas/ontologies. Comparisons and integration among NER systems, thus, becomes complex. In this paper, we propose a transfer-learning approach where we use supervised learning methods to automatically learn mappings between ontologies of NER systems, where an input probability distribution over a set of entity types defined in a source ontology is mapped to a target distribution over the entity types defined for a target ontology. Experiments conducted with benchmark data show valuable re-classification performance of entity mentions, suggesting our approach as a promising one for domain adaptation of NER systems.

Published
2017

19. UniMiB: Entity linking in tweets using Jaro-Winkler distance, popularity and coherence

Author

Radovanovic, D, Preotiuc-Pietro, D, Weller, K, Dadzie, A-S, Cano Basave, AE, Caliano, D, Fersini, E, Manchanda, P, Palmonari, M, Messina, V, FERSINI, ELISABETTA, MANCHANDA, PIKAKSHI, PALMONARI, MATTEO LUIGI, MESSINA, VINCENZINA, Radovanovic, D, Preotiuc-Pietro, D, Weller, K, Dadzie, A-S, Cano Basave, AE, Caliano, D, Fersini, E, Manchanda, P, Palmonari, M, Messina, V, FERSINI, ELISABETTA, MANCHANDA, PIKAKSHI, PALMONARI, MATTEO LUIGI, and MESSINA, VINCENZINA

Abstract

This paper summarizes the participation of UNIMIB team in the Named Entity rEcognition and Linking (NEEL) Challenge in #Microposts2016. In this paper, we propose a knowledge-base approach for identifying and linking named entities from tweets. The named entities are, further, classified using evidence provided by our entity linking algorithm and type-casted into Microposts categories.

Published
2016

20. Leveraging entity linking to enhance entity recognition in microblogs

Author

Manchanda, P, Fersini, E, Palmonari, M, MANCHANDA, PIKAKSHI, FERSINI, ELISABETTA, PALMONARI, MATTEO LUIGI, Manchanda, P, Fersini, E, Palmonari, M, MANCHANDA, PIKAKSHI, FERSINI, ELISABETTA, and PALMONARI, MATTEO LUIGI

Abstract

The Web of Data provides abundant knowledge wherein objects or entities are described by means of properties and their relationships with other objects or entities. This knowledge is used extensively by the research community for Information Extraction tasks such as Named Entity Recognition (NER) and Linking (NEL) to make sense of data. Named entities can be identified from a variety of textual formats which are further linked to corresponding resources in the Web of Data. These tasks of entity recognition and linking are, however, cast as distinct problems in the state-of-the-art, thereby, overlooking the fact that performance of entity recognition affects the performance of entity linking. The focus of this paper is to improve the performance of entity recognition on a particular textual format, viz, microblog posts by disambiguating the named entities with resources in a Knowledge Base (KB). We propose an unsupervised learning approach to jointly improve the performance of entity recognition and, thus, the whole system by leveraging the results of disambiguated entities.

Published
2015

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