Sequence-to-sequence neural architectures are the state of the art for addressing the task of correcting grammatical errors. However, large training datasets are required for this task. This paper studies the use of sequence-to-sequence neural models for the correction of grammatical errors in Basque. As there is no training data for this language, we have developed a rule-based method to generate grammatically incorrect sentences from a collection of correct sentences extracted from a corpus of 500,000 news in Basque. We have built different training datasets according to different strategies to combine the synthetic examples. From these datasets different models based on the Transformer architecture have been trained and evaluated according to accuracy, recall and F0.5 score. The results obtained with the best model reach 0.87 of F0.5 score.

This work describes the formalization of a word structure grammar that represents the complex morphological and morphosyntactic information embedded within the word forms of an agglutinative language (Basque), giving a comprehensive linguistic description of the main morphological phenomena, such as affixation, derivation, and composition, and also taking into account the modeling of both standard and non-standard words. We have identified the relevant issues to be addressed in the representation of such a grammar. We also present the development of Morfeus+, a tool for the analysis of unrestricted texts, testing its applicability and showing that its coverage is wide and robust, allowing the efficient processing of big volumes of data. This paper describes a mature system that has required several person/years and that tries to integrate a rigorous linguistic specification together with more practical implementation matters, such as the appropriate treatment of unknown words in unrestricted texts.

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.

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.

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.

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.

Interdisciplinary research between computational linguistics and the Semantic Web is increasing. The NLP community makes more and more use of information presented as Linked Data. At the same time, an increasing interest in representing information from text as Linked Data can be observed in the Semantic Web community. It is however not necessarily straightforward to adapt existing NLP modules so that they can read in and produce linguistic annotations in RDF. This paper presents the representations we use in two projects that involve both directions of interaction between NLP and the Semantic Web. In previous work, we have shown how instances represented in RDF can be linked to text and linguistic annotations using GAF. In this paper, we address how we can make further use of Linked Data by using its principles in linguistic annotations.

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.

The European project NewsReader develops advanced technology to process daily news streams in 4 languages, extracting what happened, when and where it happened and who was involved. NewsReader reads massive amounts of news coming from thousands of sources. It compares the results across sources to complement information and determine where the different sources disagree. Furthermore, it merges current news with previous news, creating a long-term history rather than separate events. The result is cumulated over time, producing an extremely large knowledge base that is visualized using new techniques to provide more comprehensive access.

Computational power needs have grown dramatically in recent years. This is also the case in many language processing tasks, due to very big quantities of documents that must be processed in a reasonable time frame. This scenario has led to a paradigm change in the computing architectures and large-scale text 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 for processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing nodes.