Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

A Multilingual Study of Compressive Cross-Language Text Summarization

Cross-Language Text Summarization (CLTS) generates summaries in a language different from the language of the source documents. Recent methods use information from both languages to generate summaries with the most informative sentences. However, these methods have performance that can vary according to languages, which can reduce the quality of summaries. In this paper, we propose a compressive framework to generate cross-language summaries. In order to analyze performance and especially stability, we tested our system and extractive baselines on a dataset available in four languages (English, French, Portuguese, and Spanish) to generate English and French summaries. An automatic evaluation showed that our method outperformed extractive state-of-art CLTS methods with better and more stable ROUGE scores for all languages

Harmonization and Merging of two Italian Dependency Treebanks

The paper describes the methodology which is currently being defined for the construction of a "Merged Italian Dependency Treebank'' (MIDT) starting from already existing resources. In particular, it reports the results of a case study carried out on two available dependency treebanks, i.e. TUT and ISST--TANL. The issues raised during the comparison of the annotation schemes underlying the two treebanks are discussed and investigated with a particular emphasis on the definition of a set of linguistic categories to be used as a "bridge'' between the specific schemes. As an encoding format, the CoNLL de facto standard is used

Graph-Based Annotation Engineering: Towards a Gold Corpus for Role and Reference Grammar

This paper describes the application of annotation engineering techniques for the construction of a corpus for Role and Reference Grammar (RRG). RRG is a semantics-oriented formalism for natural language syntax popular in comparative linguistics and linguistic typology, and predominantly applied for the description of non-European languages which are less-resourced in terms of natural language processing. Because of its cross-linguistic applicability and its conjoint treatment of syntax and semantics, RRG also represents a promising framework for research challenges within natural language processing. At the moment, however, these have not been explored as no RRG corpus data is publicly available. While RRG annotations cannot be easily derived from any single treebank in existence, we suggest that they can be reliably inferred from the intersection of syntactic and semantic annotations as represented by, for example, the Universal Dependencies (UD) and PropBank (PB), and we demonstrate this for the English Web Treebank, a 250,000 token corpus of various genres of English internet text. The resulting corpus is a gold corpus for future experiments in natural language processing in the sense that it is built on existing annotations which have been created manually. A technical challenge in this context is to align UD and PB annotations, to integrate them in a coherent manner, and to distribute and to combine their information on RRG constituent and operator projections. For this purpose, we describe a framework for flexible and scalable annotation engineering based on flexible, unconstrained graph transformations of sentence graphs by means of SPARQL Update

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

Graph-based annotation engineering: towards a gold corpus for role and reference grammar

This paper describes the application of annotation engineering techniques for the construction of a corpus for Role and Reference Grammar (RRG). RRG is a semantics-oriented formalism for natural language syntax popular in comparative linguistics and linguistic typology, and predominantly applied for the description of non-European languages which are less-resourced in terms of natural language processing. Because of its cross-linguistic applicability and its conjoint treatment of syntax and semantics, RRG also represents a promising framework for research challenges within natural language processing. At the moment, however, these have not been explored as no RRG corpus data is publicly available. While RRG annotations cannot be easily derived from any single treebank in existence, we suggest that they can be reliably inferred from the intersection of syntactic and semantic annotations as represented by, for example, the Universal Dependencies (UD) and PropBank (PB), and we demonstrate this for the English Web Treebank, a 250,000 token corpus of various genres of English internet text. The resulting corpus is a gold corpus for future experiments in natural language processing in the sense that it is built on existing annotations which have been created manually. A technical challenge in this context is to align UD and PB annotations, to integrate them in a coherent manner, and to distribute and to combine their information on RRG constituent and operator projections. For this purpose, we describe a framework for flexible and scalable annotation engineering based on flexible, unconstrained graph transformations of sentence graphs by means of SPARQL Update