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Dr. Rodrigo Agerri
IXA NLP Group, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain

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Research Keywords & Expertise

0 Information Extraction
0 Natural Language Processing
0 named entity recognition
0 Opinion Mining
0 Aspect Based Sentiment Analysis

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Natural Language Processing
named entity recognition
Aspect Based Sentiment Analysis
Sequence Labelling

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Journal article
Published: 23 February 2021 in Sustainability
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Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.

ACS Style

Ainhoa Serna; Aitor Soroa; Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability 2021, 13, 2397 .

AMA Style

Ainhoa Serna, Aitor Soroa, Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability. 2021; 13 (4):2397.

Chicago/Turabian Style

Ainhoa Serna; Aitor Soroa; Rodrigo Agerri. 2021. "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport." Sustainability 13, no. 4: 2397.

Journal article
Published: 15 January 2021 in EKAIA Euskal Herriko Unibertsitateko Zientzia eta Teknologia Aldizkaria
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Lan honen helburu nagusia, hedabideetako euskarazko edukian aipatzen diren izendun entitate nabarmenen identifikazioa da, identifikazio hau denbora errealean eginez. Horretarako, euskaraz argitaratutako albisteetatik izendun entitateak automatikoki jaso eta etiketatzeko sistema garatu da, artearen egoerako Ikasketa Sakoneko ereduak erabiliz. Izendun entitateen identifikadoreari esker, denbora errealean jasotako albisteetako izendun entitateak etengabe identifikatu eta jasotzen dira, erregistro bat osatuz. Bukatzeko, identifikatutako izendun entitate nabarmenak astero publikatzen dira Wikipediako orri batean, Euskarazko Wikipedian artikulurik ez daukaten entitate nabarmenak erakusteko asmoz.

ACS Style

Joseba Fernandez De Landa; Rodrigo Agerri. Euskarazko on-line artikuluetan aipatutako izendun entitate nabarmenen identifikazioa denbora errealean. EKAIA Euskal Herriko Unibertsitateko Zientzia eta Teknologia Aldizkaria 2021, 1 .

AMA Style

Joseba Fernandez De Landa, Rodrigo Agerri. Euskarazko on-line artikuluetan aipatutako izendun entitate nabarmenen identifikazioa denbora errealean. EKAIA Euskal Herriko Unibertsitateko Zientzia eta Teknologia Aldizkaria. 2021; ():1.

Chicago/Turabian Style

Joseba Fernandez De Landa; Rodrigo Agerri. 2021. "Euskarazko on-line artikuluetan aipatutako izendun entitate nabarmenen identifikazioa denbora errealean." EKAIA Euskal Herriko Unibertsitateko Zientzia eta Teknologia Aldizkaria , no. : 1.

Journal article
Published: 01 January 2021 in Expert Systems with Applications
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Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains.

ACS Style

Elena Zotova; Rodrigo Agerri; German Rigau. Semi-automatic generation of multilingual datasets for stance detection in Twitter. Expert Systems with Applications 2021, 170, 114547 .

AMA Style

Elena Zotova, Rodrigo Agerri, German Rigau. Semi-automatic generation of multilingual datasets for stance detection in Twitter. Expert Systems with Applications. 2021; 170 ():114547.

Chicago/Turabian Style

Elena Zotova; Rodrigo Agerri; German Rigau. 2021. "Semi-automatic generation of multilingual datasets for stance detection in Twitter." Expert Systems with Applications 170, no. : 114547.

Journal article
Published: 13 June 2019 in Information
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Social networks like Twitter are increasingly important in the creation of new ways of communication. They have also become useful tools for social and linguistic research due to the massive amounts of public textual data available. This is particularly important for less resourced languages, as it allows to apply current natural language processing techniques to large amounts of unstructured data. In this work, we study the linguistic and social aspects of young and adult people’s behaviour based on their tweets’ contents and the social relations that arise from them. With this objective in mind, we have gathered over 10 million tweets from more than 8000 users. First, we classified each user in terms of its life stage (young/adult) according to the writing style of their tweets. Second, we applied topic modelling techniques to the personal tweets to find the most popular topics according to life stages. Third, we established the relations and communities that emerge based on the retweets. We conclude that using large amounts of unstructured data provided by Twitter facilitates social research using computational techniques such as natural language processing, giving the opportunity both to segment communities based on demographic characteristics and to discover how they interact or relate to them.

ACS Style

Joseba Fernandez de Landa; Rodrigo Agerri; Iñaki Alegria. Large Scale Linguistic Processing of Tweets to Understand Social Interactions among Speakers of Less Resourced Languages: The Basque Case. Information 2019, 10, 212 .

AMA Style

Joseba Fernandez de Landa, Rodrigo Agerri, Iñaki Alegria. Large Scale Linguistic Processing of Tweets to Understand Social Interactions among Speakers of Less Resourced Languages: The Basque Case. Information. 2019; 10 (6):212.

Chicago/Turabian Style

Joseba Fernandez de Landa; Rodrigo Agerri; Iñaki Alegria. 2019. "Large Scale Linguistic Processing of Tweets to Understand Social Interactions among Speakers of Less Resourced Languages: The Basque Case." Information 10, no. 6: 212.

Conference paper
Published: 01 January 2019 in Proceedings of the 13th International Workshop on Semantic Evaluation
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ACS Style

Rodrigo Agerri. Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features. Proceedings of the 13th International Workshop on Semantic Evaluation 2019, 1 .

AMA Style

Rodrigo Agerri. Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features. Proceedings of the 13th International Workshop on Semantic Evaluation. 2019; ():1.

Chicago/Turabian Style

Rodrigo Agerri. 2019. "Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features." Proceedings of the 13th International Workshop on Semantic Evaluation , no. : 1.

Short communication
Published: 10 December 2018 in Artificial Intelligence
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In this research note we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task. The system consists of a combination of clustering features implemented on top of a simple set of shallow local features. Experiments on the well known Aspect Based Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive across languages, obtaining best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labelling tasks. The system and models generated in this work are available for public use and to facilitate reproducibility of results.

ACS Style

Rodrigo Agerri; German Rigau. Language independent sequence labelling for Opinion Target Extraction. Artificial Intelligence 2018, 268, 85 -95.

AMA Style

Rodrigo Agerri, German Rigau. Language independent sequence labelling for Opinion Target Extraction. Artificial Intelligence. 2018; 268 ():85-95.

Chicago/Turabian Style

Rodrigo Agerri; German Rigau. 2018. "Language independent sequence labelling for Opinion Target Extraction." Artificial Intelligence 268, no. : 85-95.

Journal article
Published: 01 October 2017 in Knowledge-Based Systems
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ACS Style

Egoitz Laparra; Rodrigo Agerri; Itziar Aldabe; German Rigau. Multi-lingual and Cross-lingual timeline extraction. Knowledge-Based Systems 2017, 133, 77 -89.

AMA Style

Egoitz Laparra, Rodrigo Agerri, Itziar Aldabe, German Rigau. Multi-lingual and Cross-lingual timeline extraction. Knowledge-Based Systems. 2017; 133 ():77-89.

Chicago/Turabian Style

Egoitz Laparra; Rodrigo Agerri; Itziar Aldabe; German Rigau. 2017. "Multi-lingual and Cross-lingual timeline extraction." Knowledge-Based Systems 133, no. : 77-89.

Conference paper
Published: 01 August 2017 in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empiricalexperimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.

ACS Style

Rodrigo Agerri; German Rigau. Robust Multilingual Named Entity Recognition with Shallow Semi-supervised Features (Extended Abstract). Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence 2017, 4965 -4969.

AMA Style

Rodrigo Agerri, German Rigau. Robust Multilingual Named Entity Recognition with Shallow Semi-supervised Features (Extended Abstract). Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2017; ():4965-4969.

Chicago/Turabian Style

Rodrigo Agerri; German Rigau. 2017. "Robust Multilingual Named Entity Recognition with Shallow Semi-supervised Features (Extended Abstract)." Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence , no. : 4965-4969.

Journal article
Published: 01 October 2016 in Knowledge-Based Systems
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In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale.

ACS Style

Piek Vossen; Rodrigo Agerri; Itziar Aldabe; Agata Cybulska; Marieke van Erp; Antske Fokkens; Egoitz Laparra; Anne-Lyse Minard; Alessio Palmero Aprosio; German Rigau; Marco Rospocher; Roxane Segers. NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news. Knowledge-Based Systems 2016, 110, 60 -85.

AMA Style

Piek Vossen, Rodrigo Agerri, Itziar Aldabe, Agata Cybulska, Marieke van Erp, Antske Fokkens, Egoitz Laparra, Anne-Lyse Minard, Alessio Palmero Aprosio, German Rigau, Marco Rospocher, Roxane Segers. NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news. Knowledge-Based Systems. 2016; 110 ():60-85.

Chicago/Turabian Style

Piek Vossen; Rodrigo Agerri; Itziar Aldabe; Agata Cybulska; Marieke van Erp; Antske Fokkens; Egoitz Laparra; Anne-Lyse Minard; Alessio Palmero Aprosio; German Rigau; Marco Rospocher; Roxane Segers. 2016. "NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news." Knowledge-Based Systems 110, no. : 60-85.

Journal article
Published: 01 September 2016 in Artificial Intelligence
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We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.

ACS Style

Rodrigo Agerri; German Rigau. Robust multilingual Named Entity Recognition with shallow semi-supervised features. Artificial Intelligence 2016, 238, 63 -82.

AMA Style

Rodrigo Agerri, German Rigau. Robust multilingual Named Entity Recognition with shallow semi-supervised features. Artificial Intelligence. 2016; 238 ():63-82.

Chicago/Turabian Style

Rodrigo Agerri; German Rigau. 2016. "Robust multilingual Named Entity Recognition with shallow semi-supervised features." Artificial Intelligence 238, no. : 63-82.

Journal article
Published: 01 May 2015 in Knowledge-Based Systems
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Requirements in computational power have grown dramatically in recent years. This is also the case in many language processing tasks, due to the overwhelming and ever increasing amount of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the Natural Language Processing field. In this paper we present a new distributed architecture and technology for scaling up text analysis running a complete chain of linguistic processors on several virtual machines. Furthermore, we also describe a series of experiments carried out with the goal of analyzing the scaling capabilities of the language processing pipeline used in this setting. We explore the use of Storm in a new approach for scalable distributed language processing across multiple machines and evaluate its effectiveness and efficiency when processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding language processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing nodes.

ACS Style

Rodrigo Agerri; Xabier Artola; Zuhaitz Beloki; German Rigau; Aitor Soroa. Big data for Natural Language Processing: A streaming approach. Knowledge-Based Systems 2015, 79, 36 -42.

AMA Style

Rodrigo Agerri, Xabier Artola, Zuhaitz Beloki, German Rigau, Aitor Soroa. Big data for Natural Language Processing: A streaming approach. Knowledge-Based Systems. 2015; 79 ():36-42.

Chicago/Turabian Style

Rodrigo Agerri; Xabier Artola; Zuhaitz Beloki; German Rigau; Aitor Soroa. 2015. "Big data for Natural Language Processing: A streaming approach." Knowledge-Based Systems 79, no. : 36-42.

Conference paper
Published: 01 January 2015 in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
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ACS Style

Iñaki San Vicente; Xabier Saralegi; Rodrigo Agerri. EliXa: A Modular and Flexible ABSA Platform. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) 2015, 1 .

AMA Style

Iñaki San Vicente, Xabier Saralegi, Rodrigo Agerri. EliXa: A Modular and Flexible ABSA Platform. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). 2015; ():1.

Chicago/Turabian Style

Iñaki San Vicente; Xabier Saralegi; Rodrigo Agerri. 2015. "EliXa: A Modular and Flexible ABSA Platform." Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) , no. : 1.

Conference paper
Published: 01 January 2014 in Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
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ACS Style

Iñaki San Vicente; Rodrigo Agerri; German Rigau. Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, 1 .

AMA Style

Iñaki San Vicente, Rodrigo Agerri, German Rigau. Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014; ():1.

Chicago/Turabian Style

Iñaki San Vicente; Rodrigo Agerri; German Rigau. 2014. "Simple, Robust and (almost) Unsupervised Generation of Polarity Lexicons for Multiple Languages." Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics , no. : 1.

Conference paper
Published: 01 January 2014 in Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics
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ACS Style

Rodrigo Agerri; Josu Bermudez; German Rigau. Multilingual, Efficient and Easy NLP Processing with IXA Pipeline. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, 5 -8.

AMA Style

Rodrigo Agerri, Josu Bermudez, German Rigau. Multilingual, Efficient and Easy NLP Processing with IXA Pipeline. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics. 2014; ():5-8.

Chicago/Turabian Style

Rodrigo Agerri; Josu Bermudez; German Rigau. 2014. "Multilingual, Efficient and Easy NLP Processing with IXA Pipeline." Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics , no. : 5-8.

Book chapter
Published: 27 July 2011 in Word Sense Disambiguation
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We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. Our system currently only deals with cases, which we found to be quite common in edrama, in which a person is compared to, or stated to be, something non-human such as an animal, object, artefact or supernatural being. Our approach permits a limited degree of variability and extension of these metaphors. We discuss how these metaphorical utterances are recognized, how they are analysed and their affective content determined and in particular how the electronic lexical database, WordNet, and the natural language glosses of the WordNet sysnsets can be used. We also discuss how this relatively shallow approach relates in important ways to the deeper ATT-Meta theory of metaphor interpretation and to approaches to affect and emotion in metaphor theory. We finish by illustrating the approach with a number of ‘worked examples’.

ACS Style

Alan Wallington; Rodrigo Agerri; John Barnden; Mark Lee; Tim Rumbell. Affect Transfer by Metaphor for an Intelligent Conversational Agent. Word Sense Disambiguation 2011, 53 -66.

AMA Style

Alan Wallington, Rodrigo Agerri, John Barnden, Mark Lee, Tim Rumbell. Affect Transfer by Metaphor for an Intelligent Conversational Agent. Word Sense Disambiguation. 2011; ():53-66.

Chicago/Turabian Style

Alan Wallington; Rodrigo Agerri; John Barnden; Mark Lee; Tim Rumbell. 2011. "Affect Transfer by Metaphor for an Intelligent Conversational Agent." Word Sense Disambiguation , no. : 53-66.

Conference paper
Published: 01 January 2011 in Computer Vision
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This paper describes and discusses an approach to extract and exploit enriched Named Entities for Image Photo Retrieval. The enrichment of Named Entities is inspired by the concept of definite description. The approach is evaluated using the imageCLEF-08 testset for the photo retrieval task held at Cross-Language Evaluation Forum in 2008. We are particularly interested in testing and discuss whether and how linguistic techniques such as the one presented here can be of benefit for an ad-hoc photo retrieval task. More specifically, results show an improvement in precision when Named Entities are contained in the text although for an overall improvement a better integration of these techniques in a general approach to photo retrieval is needed.

ACS Style

Rodrigo Agerri; Rubén Granados; Ana Garcia-Serrano. Enrichment of Named Entities for Image Photo Retrieval. Computer Vision 2011, 6535, 101 -110.

AMA Style

Rodrigo Agerri, Rubén Granados, Ana Garcia-Serrano. Enrichment of Named Entities for Image Photo Retrieval. Computer Vision. 2011; 6535 ():101-110.

Chicago/Turabian Style

Rodrigo Agerri; Rubén Granados; Ana Garcia-Serrano. 2011. "Enrichment of Named Entities for Image Photo Retrieval." Computer Vision 6535, no. : 101-110.

Conference paper
Published: 01 January 2010 in Computer Vision
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This paper presents an automatically generated Intermediate Logic Form of WordNet’s glosses. Our proposed logic form includes neo-Davidsonian reification in a simple and flat syntax close to natural language. We offer a comparison with other semantic representations such as those provided by Hobbs and Extended WordNet. The Intermediate Logic Forms are straightforwardly obtained from the output of a pipeline consisting of a part-of-speech tagger, a dependency parser and our own Intermediate Logic Form generator (all freely available tools). We apply the pipeline to the glosses of WordNet 3.0 to obtain a lexical resource ready to be used as knowledge base or resource for a variety of tasks involving some kind of semantic inference. We present a qualitative evaluation of the resource and discuss its possible application in Natural Language Understanding.

ACS Style

Rodrigo Agerri; Anselmo Peñas. On the Automatic Generation of Intermediate Logic Forms for WordNet Glosses. Computer Vision 2010, 6008, 26 -37.

AMA Style

Rodrigo Agerri, Anselmo Peñas. On the Automatic Generation of Intermediate Logic Forms for WordNet Glosses. Computer Vision. 2010; 6008 ():26-37.

Chicago/Turabian Style

Rodrigo Agerri; Anselmo Peñas. 2010. "On the Automatic Generation of Intermediate Logic Forms for WordNet Glosses." Computer Vision 6008, no. : 26-37.

Book chapter
Published: 01 January 2009 in Diversity and Diachrony
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ACS Style

Rodrigo Agerri; John A. Barnden; Mark Lee; Alan M. Wallington. Inference and domain independent mappings in metaphor understanding. Diversity and Diachrony 2009, 309, 259 -268.

AMA Style

Rodrigo Agerri, John A. Barnden, Mark Lee, Alan M. Wallington. Inference and domain independent mappings in metaphor understanding. Diversity and Diachrony. 2009; 309 ():259-268.

Chicago/Turabian Style

Rodrigo Agerri; John A. Barnden; Mark Lee; Alan M. Wallington. 2009. "Inference and domain independent mappings in metaphor understanding." Diversity and Diachrony 309, no. : 259-268.

Conference paper
Published: 01 January 2008 in the 2008 Conference
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ACS Style

Rodrigo Agerri; John Barnden; Mark Lee; Alan Wallington. Textual entailment as an evaluation framework for metaphor resolution. the 2008 Conference 2008, 1 .

AMA Style

Rodrigo Agerri, John Barnden, Mark Lee, Alan Wallington. Textual entailment as an evaluation framework for metaphor resolution. the 2008 Conference. 2008; ():1.

Chicago/Turabian Style

Rodrigo Agerri; John Barnden; Mark Lee; Alan Wallington. 2008. "Textual entailment as an evaluation framework for metaphor resolution." the 2008 Conference , no. : 1.

Conference paper
Published: 27 August 2007 in Computer Vision
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In this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. Such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. Furthermore, we show the role that these invariant mappings play in a semantic framework for metaphor interpretation.

ACS Style

Rodrigo Agerri; John Barnden; Mark Lee; Alan Wallington. Default Inferences in Metaphor Interpretation. Computer Vision 2007, 1 -14.

AMA Style

Rodrigo Agerri, John Barnden, Mark Lee, Alan Wallington. Default Inferences in Metaphor Interpretation. Computer Vision. 2007; ():1-14.

Chicago/Turabian Style

Rodrigo Agerri; John Barnden; Mark Lee; Alan Wallington. 2007. "Default Inferences in Metaphor Interpretation." Computer Vision , no. : 1-14.