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In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest. In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities. Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information. We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R. We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English. For instance, Spanish is more effective when training a zero-shot XNED system that disambiguates Basque mentions with respect to an English knowledge graph.
Ander Barrena; Aitor Soroa; Eneko Agirre. Towards zero-shot cross-lingual named entity disambiguation. Expert Systems with Applications 2021, 184, 115542 .
AMA StyleAnder Barrena, Aitor Soroa, Eneko Agirre. Towards zero-shot cross-lingual named entity disambiguation. Expert Systems with Applications. 2021; 184 ():115542.
Chicago/Turabian StyleAnder Barrena; Aitor Soroa; Eneko Agirre. 2021. "Towards zero-shot cross-lingual named entity disambiguation." Expert Systems with Applications 184, no. : 115542.
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.
Ainhoa Serna; Aitor Soroa; Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability 2021, 13, 2397 .
AMA StyleAinhoa Serna, Aitor Soroa, Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability. 2021; 13 (4):2397.
Chicago/Turabian StyleAinhoa Serna; Aitor Soroa; Rodrigo Agerri. 2021. "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport." Sustainability 13, no. 4: 2397.
Generating an image from its textual description requires both a certain level of language understanding and common sense knowledge about the spatial relations of the physical entities being described. In this work, we focus on inferring the spatial relation between entities, a key step in the process of composing scenes based on text. More specifically, given a caption containing a mention to a subject and the location and size of the bounding box of that subject, our goal is to predict the location and size of an object mentioned in the caption. Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object. In fact, the used evaluation datasets contain manually annotated ontological triplets but no captions, making the exercise unrealistic: a manual step was required; and systems did not leverage the richer information in captions. Here we present a system that uses the full caption, and Relations in Captions (REC-COCO), a dataset derived from MS-COCO which allows to evaluate spatial relation inference from captions directly. Our experiments show that: (1) it is possible to infer the size and location of an object with respect to a given subject directly from the caption; (2) the use of full text allows to place the object better than using a manually annotated relation. Our work paves the way for systems that, given a caption, decide which entities need to be depicted and their respective location and sizes, in order to then generate the final image.
Aitzol Elu; Gorka Azkune; Oier Lopez de Lacalle; Ignacio Arganda-Carreras; Aitor Soroa; Eneko Agirre. Inferring spatial relations from textual descriptions of images. Pattern Recognition 2021, 113, 107847 .
AMA StyleAitzol Elu, Gorka Azkune, Oier Lopez de Lacalle, Ignacio Arganda-Carreras, Aitor Soroa, Eneko Agirre. Inferring spatial relations from textual descriptions of images. Pattern Recognition. 2021; 113 ():107847.
Chicago/Turabian StyleAitzol Elu; Gorka Azkune; Oier Lopez de Lacalle; Ignacio Arganda-Carreras; Aitor Soroa; Eneko Agirre. 2021. "Inferring spatial relations from textual descriptions of images." Pattern Recognition 113, no. : 107847.
In addition to production-oriented robots, service robots with social skills can also perform a role in industrial environments, providing on-demand ancillary services that support production activities. In this paper, a robot that provides way-finding services within an industrial facility (e.g., finding a person or place) and is able to naturally interact with workers is presented. So as to give a more natural dimension to the robot’s verbal interaction ability and achieve acceptance from human workers, the research in this paper is directed towards the improvement of its semantic natural language interpreter, with the aim of making it able to automatically learn from new interactions. A user study is also reported, in order to assess both the capabilities of the interpreter and the performance of the robot’s communication and way-finding abilities, paying special attention to user experience (UX), which may help identify possible design problems in further research stages.
Cristina Aceta; Johan Kildal; Izaskun Fernández; Aitor Soroa. Towards an optimal design of natural human interaction mechanisms for a service robot with ancillary way-finding capabilities in industrial environments. Production & Manufacturing Research 2021, 9, 1 -32.
AMA StyleCristina Aceta, Johan Kildal, Izaskun Fernández, Aitor Soroa. Towards an optimal design of natural human interaction mechanisms for a service robot with ancillary way-finding capabilities in industrial environments. Production & Manufacturing Research. 2021; 9 (1):1-32.
Chicago/Turabian StyleCristina Aceta; Johan Kildal; Izaskun Fernández; Aitor Soroa. 2021. "Towards an optimal design of natural human interaction mechanisms for a service robot with ancillary way-finding capabilities in industrial environments." Production & Manufacturing Research 9, no. 1: 1-32.
Computational power needs have greatly increased during the last years, and this is also the case in the Natural Language Processing (NLP) area, where thousands of documents must be processed, i.e., linguistically analyzed, in a reasonable time frame. These computing needs have implied a radical change in the computing architectures and big-scale text processing techniques used in NLP. In this paper, we present a scalable architecture for distributed language processing. The architecture uses Storm to combine diverse NLP modules into a processing chain, which carries out the linguistic analysis of documents. Scalability requires designing solutions that are able to run distributed programs in parallel and across large machine clusters. Using the architecture presented here, it is possible to integrate a set of third-party NLP modules into a unique processing chain which can be deployed onto a distributed environment, i.e., a cluster of machines, so allowing the language-processing modules run in parallel. No restrictions are placed a priori on the NLP modules apart of being able to consume and produce linguistic annotations following a given format. We show the feasibility of our approach by integrating two linguistic processing chains for English and Spanish. Moreover, we provide several scripts that allow building from scratch a whole distributed architecture that can be then easily installed and deployed onto a cluster of machines. The scripts and the NLP modules used in the paper are publicly available and distributed under free licenses. In the paper, we also describe a series of experiments carried out in the context of the NewsReader project with the goal of testing how the system behaves in different scenarios.
Zuhaitz Beloki; Xabier Artola; Aitor Soroa. A scalable architecture for data-intensive natural language processing. Natural Language Engineering 2017, 23, 709 -731.
AMA StyleZuhaitz Beloki, Xabier Artola, Aitor Soroa. A scalable architecture for data-intensive natural language processing. Natural Language Engineering. 2017; 23 (5):709-731.
Chicago/Turabian StyleZuhaitz Beloki; Xabier Artola; Aitor Soroa. 2017. "A scalable architecture for data-intensive natural language processing." Natural Language Engineering 23, no. 5: 709-731.