Aitor Soroa

Also published as: A. Soroa, Aitor Soroa Etxabe


2021

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Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring
Aitor Ormazabal | Mikel Artetxe | Aitor Soroa | Gorka Labaka | Eneko Agirre
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary (e.g., a list of identical words) as the only form of supervision. Rather than aligning two fixed embedding spaces, our method works by fixing the target language embeddings, and learning a new set of embeddings for the source language that are aligned with them. To that end, we use an extension of skip-gram that leverages translated context words as anchor points, and incorporates self-learning and iterative restarts to reduce the dependency on the initial dictionary. Our approach outperforms conventional mapping methods on bilingual lexicon induction, and obtains competitive results in the downstream XNLI task.

2020

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Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
Jon Ander Campos | Kyunghyun Cho | Arantxa Otegi | Aitor Soroa | Eneko Agirre | Gorka Azkune
Proceedings of the 28th International Conference on Computational Linguistics

The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary (correct, incorrect) feedback. In this paper we propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. We perform simulated experiments on document classification (for development) and Conversational Question Answering datasets like QuAC and DoQA, where binary user feedback is derived from gold annotations. The results show that our method is able to improve over the initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC), and even matching in out-of-domain experiments (DoQA). Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.

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Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque
Arantxa Otegi | Aitor Agirre | Jon Ander Campos | Aitor Soroa | Eneko Agirre
Proceedings of the 12th Language Resources and Evaluation Conference

Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text. There exist a variety of datasets for English, with tens of thousands of training examples, and pre-trained language models have allowed to obtain impressive results. The goal of our research is to test the performance of CQA systems under low-resource conditions which are common for most non-English languages: small amounts of native annotations and other limitations linked to low resource languages, like lack of crowdworkers or smaller wikipedias. We focus on the Basque language, and present the first non-English CQA dataset and results. Our experiments show that it is possible to obtain good results with low amounts of native data thanks to cross-lingual transfer, with quality comparable to those obtained for English. We also discovered that dialogue history models are not directly transferable to another language, calling for further research. The dataset is publicly available.

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Give your Text Representation Models some Love: the Case for Basque
Rodrigo Agerri | Iñaki San Vicente | Jon Ander Campos | Ander Barrena | Xabier Saralegi | Aitor Soroa | Eneko Agirre
Proceedings of the 12th Language Resources and Evaluation Conference

Word embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many small companies and research groups tend to use models that have been pre-trained and made available by third parties, rather than building their own. This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora. In addition, monolingual pre-trained models for non-English languages are not always available. At best, models for those languages are included in multilingual versions, where each language shares the quota of substrings and parameters with the rest of the languages. This is particularly true for smaller languages such as Basque. In this paper we show that a number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER. This work sets a new state-of-the-art in those tasks for Basque. All benchmarks and models used in this work are publicly available.

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DoQA - Accessing Domain-Specific FAQs via Conversational QA
Jon Ander Campos | Arantxa Otegi | Aitor Soroa | Jan Deriu | Mark Cieliebak | Eneko Agirre
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.

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Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
Jan Deriu | Don Tuggener | Pius von Däniken | Jon Ander Campos | Alvaro Rodrigo | Thiziri Belkacem | Aitor Soroa | Eneko Agirre | Mark Cieliebak
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The lack of time efficient and reliable evalu-ation methods is hampering the development of conversational dialogue systems (chat bots). Evaluations that require humans to converse with chat bots are time and cost intensive, put high cognitive demands on the human judges, and tend to yield low quality results. In this work, we introduce Spot The Bot, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots. Human judges then only annotate for each entity in a conversation whether they think it is human or not (assuming there are humans participants in these conversations). These annotations then allow us to rank chat bots regarding their ability to mimic conversational behaviour of humans. Since we expect that all bots are eventually recognized as such, we incorporate a metric that measures which chat bot is able to uphold human-like be-havior the longest, i.e.Survival Analysis. This metric has the ability to correlate a bot’s performance to certain of its characteristics (e.g.fluency or sensibleness), yielding interpretable results. The comparably low cost of our frame-work allows for frequent evaluations of chatbots during their evaluation cycle. We empirically validate our claims by applying Spot The Bot to three domains, evaluating several state-of-the-art chat bots, and drawing comparisonsto related work. The framework is released asa ready-to-use tool.

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Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
Arantxa Otegi | Jon Ander Campos | Gorka Azkune | Aitor Soroa | Eneko Agirre
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

We present a Question Answering (QA) system that won one of the tasks of the Kaggle CORD-19 Challenge, according to the qualitative evaluation of experts. The system is a combination of an Information Retrieval module and a reading comprehension module that finds the answers in the retrieved passages. In this paper we present a quantitative and qualitative analysis of the system. The quantitative evaluation using manually annotated datasets contradicted some of our design choices, e.g. the fact that using QuAC for fine-tuning provided better answers over just using SQuAD. We analyzed this mismatch with an additional A/B test which showed that the system using QuAC was indeed preferred by users, confirming our intuition. Our analysis puts in question the suitability of automatic metrics and its correlation to user preferences. We also show that automatic metrics are highly dependent on the characteristics of the gold standard, such as the average length of the answers.

2019

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Analyzing the Limitations of Cross-lingual Word Embedding Mappings
Aitor Ormazabal | Mikel Artetxe | Gorka Labaka | Aitor Soroa | Eneko Agirre
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.

2018

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Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
Ander Barrena | Aitor Soroa | Eneko Agirre
Proceedings of the 22nd Conference on Computational Natural Language Learning

Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K classification tasks. This gives us the opportunity to benchmark popular text representation alternatives on this massive dataset. In order to face scarce training data we propose a simple data-augmentation technique and transfer-learning. We show that bag-of-word-embeddings are better than LSTMs for tasks with scarce training data, while the situation is reversed when having larger amounts. Transferring a LSTM which is learned on all datasets is the most effective context representation option for the word experts in all frequency bands. The experiments show that our system trained on out-of-domain Wikipedia data surpass comparable NED systems which have been trained on in-domain training data.

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The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD
Eneko Agirre | Oier López de Lacalle | Aitor Soroa
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

UKB is an open source collection of programs for performing, among other tasks, Knowledge-Based Word Sense Disambiguation (WSD). Since it was released in 2009 it has been often used out-of-the-box in sub-optimal settings. We show that nine years later it is the state-of-the-art on knowledge-based WSD. This case shows the pitfalls of releasing open source NLP software without optimal default settings and precise instructions for reproducibility.

2016

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Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation
Ander Barrena | Aitor Soroa | Eneko Agirre
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Interoperability of Annotation Schemes: Using the Pepper Framework to Display AWA Documents in the ANNIS Interface
Talvany Carlotto | Zuhaitz Beloki | Xabier Artola | Aitor Soroa
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Natural language processing applications are frequently integrated to solve complex linguistic problems, but the lack of interoperability between these tools tends to be one of the main issues found in that process. That is often caused by the different linguistic formats used across the applications, which leads to attempts to both establish standard formats to represent linguistic information and to create conversion tools to facilitate this integration. Pepper is an example of the latter, as a framework that helps the conversion between different linguistic annotation formats. In this paper, we describe the use of Pepper to convert a corpus linguistically annotated by the annotation scheme AWA into the relANNIS format, with the ultimate goal of interacting with AWA documents through the ANNIS interface. The experiment converted 40 megabytes of AWA documents, allowed their use on the ANNIS interface, and involved making architectural decisions during the mapping from AWA into relANNIS using Pepper. The main issues faced during this process were due to technical issues mainly caused by the integration of the different systems and projects, namely AWA, Pepper and ANNIS.

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Two Architectures for Parallel Processing of Huge Amounts of Text
Mathijs Kattenberg | Zuhaitz Beloki | Aitor Soroa | Xabier Artola | Antske Fokkens | Paul Huygen | Kees Verstoep
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing. The two architectures focus on different processing scenarios, namely batch-processing and streaming processing. The batch-processing scenario aims at optimizing the overall throughput of the system, i.e., minimizing the overall time spent on processing all documents. The streaming architecture aims to minimize the time to process real-time incoming documents and is therefore especially suitable for live feeds. The paper presents experiments with both architectures, and reports the overall gain when they are used for batch as well as for streaming processing. All the software described in the paper is publicly available under free licenses.

2015

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Combining Mention Context and Hyperlinks from Wikipedia for Named Entity Disambiguation
Ander Barrena | Aitor Soroa | Eneko Agirre
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Improving distant supervision using inference learning
Roland Roller | Eneko Agirre | Aitor Soroa | Mark Stevenson
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Random Walks and Neural Network Language Models on Knowledge Bases
Josu Goikoetxea | Aitor Soroa | Eneko Agirre
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Random Walks for Knowledge-Based Word Sense Disambiguation
Eneko Agirre | Oier López de Lacalle | Aitor Soroa
Computational Linguistics, Volume 40, Issue 1 - March 2014

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Exploring the use of word embeddings and random walks on Wikipedia for the CogAlex shared task
Josu Goikoetxea | Eneko Agirre | Aitor Soroa
Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)

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“One Entity per Discourse” and “One Entity per Collocation” Improve Named-Entity Disambiguation
Ander Barrena | Eneko Agirre | Bernardo Cabaleiro | Anselmo Peñas | Aitor Soroa
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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A stream computing approach towards scalable NLP
Xabier Artola | Zuhaitz Beloki | Aitor Soroa
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Computational power needs have grown dramatically in recent years. This is also the case in many language processing tasks, due to overwhelming quantities 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 NLP field. In this paper we describe a series of experiments carried out in the context of the NewsReader project with the goal of analyzing the scaling capabilities of the language processing pipeline used in it. 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.

2013

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PATHS: A System for Accessing Cultural Heritage Collections
Eneko Agirre | Nikolaos Aletras | Paul Clough | Samuel Fernando | Paula Goodale | Mark Hall | Aitor Soroa | Mark Stevenson
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Enabling the Discovery of Digital Cultural Heritage Objects through Wikipedia
Mark Michael Hall | Oier Lopez de Lacalle | Aitor Soroa Etxabe | Paul Clough | Eneko Agirre
Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Matching Cultural Heritage items to Wikipedia
Eneko Agirre | Ander Barrena | Oier Lopez de Lacalle | Aitor Soroa | Samuel Fernando | Mark Stevenson
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information. Wikipedia articles, on the contrary, include in-depth descriptions and links to related articles, which motivate the enrichment of CH items with information from Wikipedia. In this paper we explore the feasibility of finding matching articles in Wikipedia for a given Cultural Heritage item. We manually annotated a random sample of items from Europeana, and performed a qualitative and quantitative study of the issues and problems that arise, showing that each kind of CH item is different and needs a nuanced definition of what ``matching article'' means. In addition, we test a well-known wikification (aka entity linking) algorithm on the task. Our results indicate that a substantial number of items can be effectively linked to their corresponding Wikipedia article.

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Comparing Taxonomies for Organising Collections of Documents
Samuel Fernando | Mark Hall | Eneko Agirre | Aitor Soroa | Paul Clough | Mark Stevenson
Proceedings of COLING 2012

2010

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Kyoto: An Integrated System for Specific Domain WSD
Aitor Soroa | Eneko Agirre | Oier Lopez de Lacalle | Wauter Bosma | Piek Vossen | Monica Monachini | Jessie Lo | Shu-Kai Hsieh
Proceedings of the 5th International Workshop on Semantic Evaluation

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KYOTO: an open platform for mining facts
Piek Vossen | German Rigau | Eneko Agirre | Aitor Soroa | Monica Monachini | Roberto Bartolini
Proceedings of the 6th Workshop on Ontologies and Lexical Resources

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Exploring Knowledge Bases for Similarity
Eneko Agirre | Montse Cuadros | German Rigau | Aitor Soroa
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Graph-based similarity over WordNet has been previously shown to perform very well on word similarity. This paper presents a study of the performance of such a graph-based algorithm when using different relations and versions of Wordnet. The graph algorithm is based on Personalized PageRank, a random-walk based algorithm which computes the probability of a random-walk initiated in the target word to reach any synset following the relations in WordNet (Haveliwala, 2002). Similarity is computed as the cosine of the probability distributions for each word over WordNet. The best combination of relations includes all relations in WordNet 3.0, included disambiguated glosses, and automatically disambiguated topic signatures called KnowNets. All relations are part of the official release of WordNet, except KnowNets, which have been derived automatically. The results over the WordSim 353 dataset show that using the adequate relations the performance improves over previously published WordNet-based results on the WordSim353 dataset (Finkelstein et al., 2002). The similarity software and some graphs used in this paper are publicly available at http://ixa2.si.ehu.es/ukb.

2009

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Personalizing PageRank for Word Sense Disambiguation
Eneko Agirre | Aitor Soroa
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches
Eneko Agirre | Enrique Alfonseca | Keith Hall | Jana Kravalova | Marius Paşca | Aitor Soroa
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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WikiWalk: Random walks on Wikipedia for Semantic Relatedness
Eric Yeh | Daniel Ramage | Christopher D. Manning | Eneko Agirre | Aitor Soroa
Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)

2008

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Using the Multilingual Central Repository for Graph-Based Word Sense Disambiguation
Eneko Agirre | Aitor Soroa
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents the results of a graph-based method for performing knowledge-based Word Sense Disambiguation (WSD). The technique exploits the structural properties of the graph underlying the chosen knowledge base. The method is general, in the sense that it is not tied to any particular knowledge base, but in this work we have applied it to the Multilingual Central Repository (MCR). The evaluation has been performed on the Senseval-3 all-words task. The main contributions of the paper are twofold: (1) We have evaluated the separate and combined performance of each type of relation in the MCR, and thus indirectly validated the contents of the MCR and their potential for WSD. (2) We obtain state-of-the-art results, and in fact yield the best results that can be obtained using publicly available data.

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Spelling Correction: from Two-Level Morphology to Open Source
Iñaki Alegria | Klara Ceberio | Nerea Ezeiza | Aitor Soroa | Gregorio Hernandez
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Basque is a highly inflected and agglutinative language (Alegria et al., 1996). Two-level morphology has been applied successfully to this kind of languages and there are two-level based descriptions for very different languages. After doing the morphological description for a language, it is easy to develop a spelling checker/corrector for this language. However, what happens if we want to use the speller in the “free world” (OpenOffice, Mozilla, emacs, LaTeX, etc.)? Ispell and similar tools (aspell, hunspell, myspell) are the usual mechanisms for these purposes, but they do not fit the two-level model. In the absence of two-level morphology based mechanisms, an automatic conversion from two-level description to hunspell is described in this paper.

2007

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SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination Systems
Eneko Agirre | Aitor Soroa
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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UBC-AS: A Graph Based Unsupervised System for Induction and Classification
Eneko Agirre | Aitor Soroa
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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Two graph-based algorithms for state-of-the-art WSD
Eneko Agirre | David Martínez | Oier López de Lacalle | Aitor Soroa
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm
Eneko Agirre | David Martínez | Oier López de Lacalle | Aitor Soroa
Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing

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Structure, Annotation and Tools in the Basque ZT Corpus
N. Areta | A. Gurrutxaga | I. Leturia | Z. Polin | R. Saiz | I. Alegria | X. Artola | A. Diaz de Ilarraza | N. Ezeiza | A. Sologaistoa | A. Soroa | A. Valverde
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The ZT corpus (Basque Corpus of Science and Technology) is a tagged collection of specialized texts in Basque, which wants to be a main resource in research and development about written technical Basque: terminology, syntax and style. It will be the first written corpus in Basque which will be distributed by ELDA (at the end of 2006) and it wants to be a methodological and functional reference for new projects in the future (i.e. a national corpus for Basque). We also present the technology and the tools to build this Corpus. These tools, Corpusgile and Eulia, provide a flexible and extensible infrastructure for creating, visualizing and managing corpora and for consulting, visualizing and modifying annotations generated by linguistic tools.

2002

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A Class Library for the Integration of NLP Tools: Definition and implementation of an Abstract Data Type Collection for the manipulation of SGML documents in a context of stand-off linguistic annotation
X. Artola | A. Díaz de Ilarraza | N. Ezeiza | K. Gojenola | G. Hernández | A. Soroa
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2000

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A Proposal for the Integration of NLP Tools using SGML-Tagged Documents
X. Artola | A. Díaz de Ilarraza | N. Ezeiza | K. Gojenola | A. Maritxalar | A. Soroa
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)