Information retrieval (IR) is the task of finding relevant documents in response to a user query.Although Spanish is the second most spoken native language, there are few Spanish IR datasets, which limits the development of information access tools for Spanish speakers.We introduce MessIRve, a large-scale Spanish IR dataset with almost 700,000 queries from Google's autocomplete API and relevant documents sourced from Wikipedia.MessIRve's queries reflect diverse Spanish-speaking regions, unlike other datasets that are translated from English or do not consider dialectal variations.The large size of the dataset allows it to cover a wide variety of topics, unlike smaller datasets.We provide a comprehensive description of the dataset, comparisons with existing datasets, and baseline evaluations of prominent IR models.Our contributions aim to advance Spanish IR research and improve information access for Spanish speakers.

Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.

Comunicació presentada a: The 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017), celebrada a Estocolm, Suència, del 20 al 24 d'agost de 2017.

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.

Current data warehouse and OLAP (Kimball and Ross, 2002) technologies can be efficiently applied to analyze the huge amounts of structured data that companies produce. These organizations also produce many text documents and use the Web as their largest source of external information. Although these documents include highly valuable information that should also be exploited by companies, they cannot be analyzed by current OLAP technologies because they are unstructured and mainly contain text. The current trend is to find these documents available in XML-like formats. Our proposal is to build XML document warehouses that can be used by companies to store unstructured information coming from their internal and external sources. In (Perez et al., 2005) we proposed an architecture for the integration of a corporate warehouse of structured data with a warehouse of text-rich XML documents. We call the resulting warehouse a contextualized warehouse. Since the XML document warehouse may contain documents about many different topics, we apply well-known information retrieval (IR) (Baeza-Yates and Ribeiro-Neto, 1999) techniques to select the context of analysis from the document warehouse. First, the user specifies an analysis context by supplying a sequence of keywords (e.g., an IR condition like "financial crisis"). Then, the analysis is performed on a so-called R-cube (Relevance cube), which is materialized by retrieving the documents and facts related to the selected context. Each fact in the R-cube will be linked to the set of documents that describe its context, and will have assigned a numerical value representing its relevance with respect to the specified context (e.g., how important the fact is for a "financial crisis"). In (Perez et al., 2005) we provided R-cubes with a data model and an algebra. This paper presents a prototype R-cube system, and explains how to use it.

Current data warehouse and OLAP technologies can be applied to analyze the structured data that companies store in their databases. The circumstances that describe the context associated with these data can be found in other internal and external sources of documents. In this paper we propose to combine the traditional corporate data warehouse with a document warehouse, resulting in a contextualized warehouse. Thus, contextualized warehouses keep a historical record of the fact and their contexts as described by the documents. In this framework, the user selects an analysis context which is represented as a novel type of OLAP cube, here called R-cube. R-cubes are characterized by two special dimensions, namely: the relevance and the context dimensions. The first dimension measures the relevance of each fact in the selected analysis context, whereas the second one relates each fact with the documents that explain their circumstances. In this work we extend an existing multi-dimensional data model and algebra for representing the R-cubes.