Gerard Casamayor

From ontology to NL: generation of multilingual user-oriented environmental reports

Natural Language Generation (NLG) from knowledge bases (KBs) has repeatedly been subject of research. However, most proposals tend to have in common that they start from KBs of limited size that either already contain linguistically-oriented knowledge structures or to whose structures different ways of realization are explicitly assigned. To avoid these limitations, we propose a three layer OWL-based ontology framework in which domain, domain communication and linguistic knowledge structures are clearly separated and show how a large scale instantiation of this framework in the environmental domain serves multilingual NLG.

Building an Environmental Information System for Personalized Content Delivery

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Perspective-oriented generation of football match summaries: Old tasks, new challenges

Team sports commentaries call for techniques that are able to select content and generate wordings to reflect the affinity of the targeted reader for one of the teams. The existing works tend to have in common that they either start from knowledge sources of limited size to whose structures then different ways of realization are explicitly assigned, or they work directly with linguistic corpora, without the use of a deep knowledge source. With the increasing availability of large-scale ontologies this is no longer satisfactory: techniques are needed that are applicable to general purpose ontologies, but which still take user preferences into account. We take the best of both worlds in that we use a two-layer ontology. The first layer is composed of raw domain data modelled in an application-independent base OWL ontology. The second layer contains a rich perspective generation-motivated domain communication knowledge ontology, inferred from the base ontology. The two-layer ontology allows us to take into account user perspective-oriented criteria at different stages of generation to generate perspective-oriented commentaries. We show how content selection, discourse structuring, information structure determination, and lexicalization are driven by these criteria and how stage after stage a truly user perspective-tailored summary is generated. The viability of our proposal has been evaluated for the generation of football match summaries of the First Spanish Football League. The reported outcome of the evaluation demonstrates that we are on the right track.

FootbOWL: using a generic ontology of football competition for planning match summaries

We present a two-layer OWL ontology-based Knowledge Base (KB) that allows for flexible content selection and discourse structuring in Natural Language text Generation (NLG) and discuss its use for these two tasks. The first layer of the ontology contains an applicationindependent base ontology. It models the domain and was not designed with NLG in mind. The second layer, which is added on top of the base ontology, models entities and events that can be inferred from the base ontology, including inferable logico-semantic relations between individuals. The nodes in the KB are weighted according to learnt models of content selection, such that a subset of them can be extracted. The extraction is done using templates that also consider semantic relations between the nodes and a simple user profile. The discourse structuring submodule maps the semantic relations to discourse relations and forms discourse units to then arrange them into a coherent discourse graph. The approach is illustrated and evaluated on a KB that models the First Spanish Football League.

Personalized Environmental Service Orchestration for Quality of Life Improvement

Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. From the technical viewpoint, the system discovers and processes reliable data in the web in order to convert them into knowledge. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland showing the impact it can have on the quality of everyday life.

Using genre-specific features for patent summaries

Targeted summarization technique for patent material.Segment as intra-sentence summarization unit.Exploitation of lexical chains across the whole patent document.Full-fledged text generation techniques for summarization. Patent search is recall-driven, which goes hand in hand with at least a partial sacrifice of precision. As a consequence, patent analysts have to regularly view and examine a large amount of patents. This implies a very high workload. Interactive analysis aids that help to minimize this workload are thus of high demand. Still, these aids do not reduce the amount of the material to be examined, they only facilitate its examination. Its reduction can be achieved working with patent summaries instead of full patent documents. So far, high quality patent summaries are produced mainly manually and only a few research works address the problem of automatic patent summarization. Most often, these works either replicate the summarization metrics known from general discourse summarization or focus on the claims of a patent. However, it can be observed that neither of the strategies is adequate: general discourse state-of-the-art summarization techniques are of limited use due to the idiosyncrasies of the patent genre, and techniques that focus on claims only miss in their summaries important details provided in the other sections on the components of the invention introduced in the claims. We propose a patent summarization technique that takes the idiosyncrasies of the patent genre (such as the unbalanced distribution of the content across the different sections of a patent, excessive length of the sentences in the claims, abstract vocabulary, etc.) into account to obtain a comprehensive summary of the invention. In particular, we make use of lexical chains in the claims and in the description of the invention and of aligned claimdescription segments at the subsentential level to assess the relevance of the individual fragments of the document for the summary. The most relevant fragments are selected and merged using full-fledged natural language generation techniques.

Personalized Environmental Service Configuration and Delivery Orchestration: The PESCaDO Demonstrator

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this demonstration, we present an environmental information system that addresses this demand in its full complexity in the context of the PESCaDO EU project. Specifically, we will show a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-based knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

MULTISENSOR: Development of multimedia content integration technologies for journalism, media monitoring and international exporting decision support.

This paper presents an overview and the first results of the FP7 MULTISENSOR project, which deals with multidimensional content integration of multimedia content for intelligent sentiment enriched and context oriented interpretation. MULTISENSOR aims at providing unified access to multilingual, multimedia and multicultural economic, news story material across borders in order to support journalism and media monitoring tasks and provide decision support for internationalisation of companies.

A multimodal analytics platform for journalists analysing large-scale, heterogeneous multilingual and multimedia content

Analysts and journalists face the problem of having to deal with very large, heterogeneous, and multilingual data volumes that need to be analyzed, understood, and aggregated. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing satisfaction from the user side.