Consumer spending is an important macroeconomic indicator that is used by policy-makers to judge the health of an economy. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.

With the appearance of Semantic Web technologies, it becomes possible to develop novel, sophisticated question answering systems, where ontologies are usually used as the core knowledge component. In the EU-funded project, QALL-ME, a domain-specific ontology was developed and applied for question answering in the domain of tourism, along with the assistance of two upper ontologies for concept expansion and reasoning. This paper focuses on the development of the QALL-ME ontology in the tourism domain and its alignment with the upper ontologies - WordNet and SUMO. The design of the ontology is presented in the paper, and a semi-automatic alignment procedure is described with some alignment results given as well. Furthermore, the aligned ontology was used to semantically annotate original data obtained from the tourism web sites and natural language questions. The storage schema of the annotated data and the data access method for retrieving answers from the annotated data are also reported in the paper.

Convergence and simplification are two of the so-called universals in translation studies. The first one postulates that translated texts tend to be more similar than non-translated texts. The second one postulates that translated texts are simpler, easier-to-understand than non-translated ones. This paper discusses the results of a project which applies NLP techniques over comparable corpora of translated and non-translated texts in Spanish seeking to establish whether these two universals hold Corpas Pastor (2008).

Present-day machine translation technologies crucially depend on the size and quality of lexical resources. Much of recent research in the area has been concerned with methods to build bilingual dictionaries automatically. In this paper we propose a methodology for the automatic detection of cognates between two languages based solely on the orthography of words. From a set of known cognates, the method induces rules capturing regularities of orthographic mutations that a word undergoes when migrating from one language into the other. The rules are then applied as a preprocessing step before measuring the orthographic similarity between putative cognates. As a result, the method allows to achieve an improvement in the F-measure of 11,86% in comparison with detecting cognates based only on the edit distance between them.