Award – Ixa Group. Language Technology.

Eneko Agirre and Mikel Artetxe awarded with Spanish National Awards on Informatics

Kepa Sarasola — Fri, 28 May 2021 18:08:10 +0000

The members of Ixa Group Eneko Agirre and Mikel Artetxe have received two spanish leading SCIE awards of 2021. Eneko has received the National Award on Informatics and Mikel has been one of the six awards for young researchers.

The jury explained that Eneko Agirre has been awarded for his excellent contributions in the field of Natural Language Processing, especially in the exploitation of large textual resources and also in unsupervised machine learning based on neuronal computing applied to machine translation. In addition to the excellent quality of his scientific publications (some of them considered to be global references), the jury remarked the great transfer of research results, his scientific leadership (Head of the HiTZ research center), and his international trajectory, both in training and collaborations.

For Mikel Artetxe has been one of the six Computer Awards for Young Researchers. The jury highly valued Mikel for his his wide international presence, his stays in the main companies in the field of language processing and his contributions to prestigious congresses.

Zorionak! Congratulations to Eneko and Mikel!

Ixa group has been awarded in the CAPITEL@IberLEF2020 competition

Kepa Sarasola — Thu, 28 May 2020 09:04:11 +0000

The three systems presented by IXA Group (HiTZ center) to the competition CAPITEL@IberLEF2020 have ranked first in Sub-task 1 (Named Entity Recognition and Classification in Spanish News Articles). The systems were developed by Rodrigo Agerri with the help of German Rigau, Ander Barrena and Jon Ander Campos.

Zorionak, congratulations to Rodrigo and all the team!

Within the framework of the PlanTL, the Royal Spanish Academy (RAE) and the Secretariat of State for Digital Advancement (SEAD) of the Ministry of Economy signed an agreement for developing a linguistically annotated corpus of Spanish news articles, aimed at expanding the language resource infrastructure for the Spanish language. The name of such corpus is CAPITEL (Corpus del Plan de Impulso a las Tecnologías del Lenguaje}, and is composed of contemporary news articles thanks to agreements with a number of news media providers. CAPITEL has three levels of linguistic annotation: morphosyntactic (with lemmas and Universal Dependencies-style POS tags and features), syntactic (following Universal Dependencies v2), and named entities.

The linguistic annotation of a subset of the CAPITEL corpus has been revised using a machine-annotation-followed-by-human-revision procedure. Manual revision has been carried out by a team of graduated linguists using the Annotation Guidelines created specifically for CAPITEL. The named entity and syntactic layers of revised annotations comprise about 1 million words for the former, and roughly 250,000 for the latter.

Due to the size of the corpus and the nature of the annotations, they proposed two IberLEF sub-tasks under the more general, umbrella task of CAPITEL @ IberLEF 2020, where they used the revised subset of the CAPITEL corpus in two challenges, namely:

(1) Named Entity Recognition and Classification and

(2) Universal Dependency Parsing.

Master students won EHealth-KD-2020 subtask on Relation Extraction

Kepa Sarasola — Mon, 11 May 2020 07:34:56 +0000

Oscar Sainz and Edgar Andrés, students of the HAP-LAP master, obtained an excellent result in the eHealth-2020 challenge presented with professors Oier Lopez de Lacalle and Aitziber Atutxa. Their team (IXA-NER-RE) has been “champion” in the Relational Extraction sub-task.

Although their main objective was participation only in the Relation Extraction subtask, they also presented tiny systems in the other two subtasks (Entity Recognition and Alternative Domain) and so their system was fourth in the main evaluation.

ZORIONAK, CONGRATULATIONS!
You have done a good job!

The results can be consulted here:
https://knowledge-learning.github.io/ehealthkd-2020/results

IXA-NER-RE was the “champion” in “Relation Extraction” subtask

The Ixa research group has been awarded in the artificial intelligence competition promoted by the US government related to COVID-19 disease

ITZIAR GONZALEZ DIOS — Thu, 07 May 2020 12:37:36 +0000

The competition CORD-19 (COVID-19 Open Research Dataset Challenge) has been organized by several organizations such as Allen Institute for AI, Chan Zuckerberg Initiative, Georgetown University, Microsoft Research, National Institutes of Health and The White House Office of Science and Technology Policy. The organization has made available to the global research community more than 50,000 scientific articles on COVID-19, SARS-CoV-2 and other coronavirus. At the same time, they issue a call to action to artificial intelligence researchers to apply the recent advances in natural language processing, in order to help scientists fighting COVID-19 disease to find necessary information in the scientific literature.

In the first phase of the competition there were 10 awards, and the system developed in the Ixa group of the HITZ centre has been awarded with one of them. Researchers from the University of the Basque Country Arantxa Otegi and Jon Ander Campos and professors Eneko Agirre and Aitor Soroa participated in the development of the system. The developed system finds answers to high priority questions from experts related to COVID-19 disease and the SARS-CoV-2 virus analyzing the aforementioned scientific articles. Thus, this system is useful for finding answers to questions such as the history of coronavirus, the transmission and diagnosis of the virus, the prevention measures in the contact between humans and animals and the lessons of previous epidemiological studies. The results of the system have been evaluated by a group of experts from the NIH of the United States and it has been selected as the system that has best answered a set of questions on the topic “What do we know about diagnostics and surveillance?”. The answers given by the system can be seen here.

See here some examples

Eneko Agirre won for the third consecutive year the Google prize

Kepa Sarasola — Wed, 01 Apr 2020 11:31:36 +0000

Eneko Agirre won again a Google prize last March. He is one of the few researchers who has obtained the Google Faculty Research Award on three occasions. The $62,000 prize will fund the project ‘Conversational Question Answering agents that learn after deployment’ to develop user dialogue systems, chatbots and artificial intelligence.

Eneko Agirre, member of Ixa Group and professor at the Faculty of Computer Science of the UPV/EHU, is the director of the newly created HiTZ Research Center. The other 6 colleagues in the project are professors Aitor Soroa and Gorka Azkune, researcher Arantxa Otegi, doctoral student Jon Ander Campos, student of Master in Language Analysis and Processing Aitor Agirre and student of Degree in Computer Science Eduardo Vallejo.

Although the project focuses mainly on English dialogues (questions about cooking and food), they are also working with Basque dialogues. For this purpose, last year Ixa Group launched a campaign to recruit volunteers for the collection of interviews in Basque. The campaign was succesfull and many personal interviews were collected in Basque (http://ixa.eus/lagundu).

La Inteligencia Artificial no es ciencia-ficción
– eitb (Ganbara) –

GOOGLE PREMIA A ENEKO AGIRRE, PROFESOR DE INFORMÁTICA DE LA UPV
– Estrategia empresarial –

Google premia una investigación española sobre procesamiento del lenguaje
– España buenas noticias –

EHUko irakasle batek Google erakundearen ikerketa sarietako bat lortu du
– Europapress –

EHUko irakasle batek Google erakundearen ikerketa sarietako bat lortu du
– El Correo –

Eneko Agirre UPV/EHUko irakasleak Google-ren sari bat lortu du, 62.000 eurokoa
– naiz –

UPV/EHUko Eneko Agirre irakasleak Google erakundearen ikerketa sarietako bat lortu du
– Campusa –

EHUko Informatika irakasle batek Google erakundearen ikerketa sarietako bat lortu du
– Bilbao24h –

One of the best three papers on Clinical NLP in 2017 was published by Ixa Group

Kepa Sarasola — Fri, 28 Jun 2019 19:51:40 +0000

A paper written by IXA members Arantza Casillas, Koldo Gojenola, Maite Oronoz and Alicia Perez, among the 3 best papers published in 2017 in the field of clinical Natural Language Processing.

The paper entitled “Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora“, by Pérez A, Weegar R, Casillas A, Gojenola K, Oronoz M, Dalianis H., published in the Journal of Biomedical Informatics , was considered one of the best three papers in the field of clinical Natural Language Processing in 2017.

A survey of the literature was performed in bibliographic databases. PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017.

The paper addresses “medical named entity recognition in clinical text in Spanish and Swedish; furthermore, they emphasize methods’ contribution in a context where little training data is available, which is often the case for languages other than English or when a new medical specialty is explored”.

The selection process is described and published in “Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook“, by Aurélie Névéol, Pierre Zweigenbaum, in the Yearbook of Medical Informatics,

Mitxelena Award for PhD theses 2018 to Olatz Perez-de-Viñaspre: Automatic medical term generation

Kepa Sarasola — Fri, 24 May 2019 12:04:04 +0000

Our colleague Olatz Perez de Viñaspre won last week the VI. Koldo MItxelena Award for PhD Theses organized by Euskaltzaindia (the Academy of Basque Language) and the University of the Basque Country.
CONGRATULATIONS Olatz!

This thesis faced the creation of computational tools to promote the use of Basque in helath services.

The winners of Mitxelena Awards 2018

Title: Automatic medical term generation fora low-resource language: translation of SNOMED CT into Basque (pdf)
Supervisors: Arantza Diaz de Ilarraza and Maite Oronoz
Publications in English:

Design of EuSnomed:
- Perez-de-Viñaspre O., and Oronoz M.Translating SNOMEDCT Terminology into a Minor Language.Proceedings ofthe 5th International Workshop on Health Text Mining and Infor-mation Analysis (Louhi), 38–45. Association for ComputationalLinguistics. Gothenburg, Sweden, 2014.
- Perez-de-Viñaspre O., and Oronoz M.An XML Based TBXFramework to Represent Multilingual SNOMED CT forTranslation.12th Mexican International Conference on Artifi-cial Intelligence, MICAI 2013. Lecture Notes in Artificial Intel-ligence, vol. 8265, 419–429. Springer, ISBN 978-3-642-45113-3.Mexico DF, Mexico. 2013
Sinple terms: lexical resources and neoclassical terms:
- Perez-de-Viñaspre O., Oronoz M., Agirrezabal M., and LersundiM.A finite state approach to translate SNOMED CTterms into Basque using medical prefixes and suffixes.Proceedings of the 11th International Conference on Finite StateMethods and Natural Language Processing, 99–103. St Andrews,Scotland, 2013.7
- Perez-de-Viñaspre O., and Oronoz M.SNOMED CT in a lan-guage isolate: an algorithm for a semiautomatic transla-tion.BMC medical informatics and decision making, volume 15,number 2, S5. BioMed Central. 2015.
Complex terms: nested terms and automatic translator:
- Perez-de-Viñaspre O., and Oronoz M.Osasun-zientzietako ter-minologiaren euskaratze automatikoaren ebaluazioa, os-asungintzako euskal komunitatea inplikatuz.II. IkerGazte,Nazioarteko Ikerketa Euskaraz. Udako Euskal Unibertsitatea. IruÃśea,Basque Country, 2017.
Other papers:
- Perez-de-Viñaspre O., Oronoz M., and Patrick J.Osasun-txostenelebidunak posible ote?I. IkerGazte, Nazioarteko Ikerketa Eu-skaraz, 730–738. Udako Euskal Unibertsitatea, ISBN 978-84-8438-539-4. Durango, Basque Country, 2015. IkerGazte Special Award.
- Perez-de-Viñaspre O., and Labaka G.IXA Biomedical TranslationSystem at WMT16 Biomedical Translation Task.Proceedingsof the First Conference on Machine Translation (WMT16), 477–482.Association for Computational Linguistics. Berlin, Germany, 2016

Eneko Agirre awarded by Google Research

Kepa Sarasola — Fri, 24 May 2019 11:27:40 +0000

Faculty Research Awards recipients (Google, 2018)

Google Faculty Research Awards 2018 were published in March. Eneko Agirre was awarded with a Google’s Faculty Research Award after its annual open call for proposals on computer science and related topics including quantum computing, machine learning, algorithms and theory, natural language processing and more. Google received 910 proposals covering 40 countries and over 320 universities. After expert reviews and committee discussions, they decided to fund 158 projects, twelve of then related to Natural Language Processing. (See: Google Faculty Research Awards 2018)

Eneko will expend the $80.000 prize in research on “Accessing FAQ and CQA sites via dialogue”

This is not his first Google award, he received another one in 2015 to work on “Learning Interlingual Representations of Words and Concepts.”

The methods that support this kind of research are taught in Eneko Agirre’s course in the Master “Language Analysis and Processing” at the Faculty of Informatics of the University of the Basque Country in Donostia.

CONGRATULATIONS Eneko!

Best Paper Award on CoNLL2018

Kepa Sarasola — Thu, 08 Nov 2018 19:42:57 +0000

Last week our colleagues Mikel Artetxe, Gorka Labaka, Iñigo Lopez-Gazpio, and Eneko Agirre were the recipients of the Best Paper Award in the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) for the paper “Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation”.

Congratulations!

Abstract:

Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness. In this paper, we show that each embedding model captures more information than directly apparent. A linear transformation that adjusts the similarity order of the model without any external resource can tailor it to achieve better results in those aspects, providing a new perspective on how embeddings encode divergent linguistic information. In addition, we explore the relation between intrinsic and extrinsic evaluation, as the effect of our transformations in downstream tasks is higher for unsupervised systems than for supervised ones.

UncoVec:
This is an open source implementation in GitHub of our word embedding post-processing and evaluation framework, described in the paper.

Science journal: ‘Ixa opens a new research avenue: Machine Translation without a dictionary?’

Kepa Sarasola — Wed, 29 Nov 2017 21:03:14 +0000

Science reported this week about the work recently published by our colleagues Mikel Artetxe, Eneko Agirre and Gorka Labaka: Artificial intelligence goes bilingual—without a dictionary

In October the 30th our three colleagues published a pre-print paper entitled Unsupervised Neural Machine Translation in collaboration with Kyunghyun Cho.

One day later G. Lample published another paper with similar contents entitled Unsupervised Machine Translation Using Monolingual Corpora Only. Both papers are under consideration at ICLR 2018.

Those are some sentences written by Matthew Hutson a freelance writer covering technology for Science:

[…] two new papers show that neural networks can learn to translate with no parallel texts—a surprising advance that could make documents in many languages more accessible.

[…] Imagine that you give one person lots of Chinese books and lots of Arabic books—none of them overlapping—and the person has to learn to translate Chinese to Arabic. That seems impossible, right?” says the first author of one study, Mikel Artetxe, a computer scientist at the University of the Basque Country (UPV) in San Sebastián, Spain. “But we show that a computer can do that.”

[…] “This is in infancy,” Artetxe’s co-author Eneko Agirre cautions. “We just opened a new research avenue, so we don’t know where it’s heading.”

[…] Artetxe says the fact that his method and Lample’s—uploaded to arXiv within a day of each other—are so similar is surprising. “But at the same time, it’s great. It means the approach is really in the right direction.”

Congratulations Mikel, Eneko, Gorka and Kyunghyun!