Alejandro Ramos-Soto

On the role of linguistic descriptions of data in the building of natural language generation systems

This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field - and, more specifically, the data-to-text sub-field - and the linguistic descriptions of data field. Both approaches are explained in a detailed description which includes: i) a methodological revision of both fields including basic concepts and definitions, models and evaluation procedures; ii) the most relevant systems, use cases and real applications described in the literature. Some reflections about the current state and future trends of each field are also provided, followed by several remarks that conclude by hinting at some potential points of mutual interest and convergence between both fields.

Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data

We present in this paper an application that automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data, together with a natural language generation (NLG) system. The application, which is named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the NLG system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology, which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation, GALiWeather will be released as a real service, offering custom forecasts for a wide public.Lists recently published books in the area of computational intelligence.

On the role of fuzzy quantified statements in linguistic summarization of data

In this paper a discussion on some of the key problems in fuzzy linguistic summarization of data in introduced. Also a description on the stages that the summarization process involving fuzzy quantified statements should accomplish is presented.

Fuzzy sets across the natural language generation pipeline

We explore the implications of using fuzzy techniques (mainly those commonly used in the linguistic description/summarization of data discipline) from a natural language generation perspective. For this, we provide an extensive discussion of some general convergence points and an exploration of the relationship between the different tasks involved in the standard natural language generation system pipeline architecture and the most common fuzzy approaches used in linguistic summarization/description of data, such as fuzzy quantified statements, evaluation criteria or aggregation operators. Each individual discussion is illustrated with a related use case. Recent work made in the context of cross-fertilization of both research fields is also referenced.

Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data

We present in this paper an application that automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data, together with a natural language generation (NLG) system. The application, which is named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the NLG system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology, which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation, GALiWeather will be released as a real service, offering custom forecasts for a wide public.

Natural language generation and fuzzy sets: An exploratory study on geographical referring expression generation

We explore how the problem of uncertainty and imprecision in natural language generation (NLG) could be addressed through the use of fuzzy sets. We propose bringing together standard empirical procedures for knowledge acquisition in NLG and computing with words/perceptions related techniques (with a special focus on linguistic description of data) to address an open challenge in NLG: the generation of geographical referring expressions. Following this methodology, we present an exploratory experiment which provides some insights about how human subjects refer to geographical expressions and discuss how the obtained results might relate to the use of fuzzy sets.

Evaluation of a Data-To-Text System for Verbalizing a Learning Analytics Dashboard

The SoftLearn Activity Reporter is a data‐to‐text service, which automatically generates textual reports about the activity developed by students within the SoftLearn virtual learning environment. In this paper, we describe the conception of the service, its architecture, and its subsequent evaluation by an expert pedagogue, where 20 full reports generated from real data from an undergraduate course supported by the SoftLearn platform were assessed. Results show that the automatically generated reports are a valuable complementary tool for explaining teachers and students the information comprised in a learning analytics dashboard.

A model based on computational perceptions for the generation of linguistic descriptions of data

In this paper we present a general model for building linguistic descriptions of data (LDD) solutions, which is based on computational models of perception inspired in the computational theory of perceptions (CTP) and in fields of knowledge different from the computational intelligence area. The elements in the model aim to consider the richness and complexity that real LDD processes are endowed with and their actual role in data-to-text natural language generation (D2T-NLG) systems. As an example of application, the building of a LDD solution in the meteorology realm is presented.

Automatic Generation of Air Quality Index Textual Forecasts Using a Data-To-Text Approach

In this paper we present a data-to-text service which automatically produces textual forecasts about the air quality state for every municipality in Galicia NW Spain for the Galician Meteorology Agency MeteoGalicia. We discuss the context and the details about the conception of the service, as well as a technical and formal description of the solution adopted. This approach complements and is integrated into GALiWeather, a public service which currently issues in Meteogalicias web page daily textual short-term weather forecasts including information about the sky state, precipitation, wind and temperatures.

Data Mining for Automatic Linguistic Description of Data

In this paper we present the results and performance of five different classifiers applied to the task of automatically generating textual weather forecasts from raw meteorological data. The type of forecasts this methodology can be applied to are template-based ones, which can be transformed into an intermediate language that can directly mapped to classes (or values of variables). Experimental validation and tests of statistical significance were conducted using nine datasets from three real meteorological publicly accessible websites, showing that RandomForest, IBk and PART are statistically the best classifiers for this task in terms of F-Score, with RandomForest providing slightly better results.