Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented

Treebanking in VIT: from Phrase Structure to Dependency Representation

In this chapter, we are dealing with treebanks and their applications. We describe VIT (Venice Italian Treebank), focusing on the syntactic-semantic features of the treebank that are partly dependent on the adopted tagset, partly on the reference linguistic theory, and, lastly - as in every treebank - on the chosen language: Italian. By discussing examples taken from treebanks available in other languages, we show the theoretical and practical differences and motivations that underlie our approach. Finally, we discuss the quantitative analysis of the data of our treebank and compare them to other treebanks. In general, we try to substantiate the claim that treebanking grammars or parsers strongly depend on the chosen treebank; and eventually this process seems to depend both on factors such as the adopted linguistic frame-work for structural description and, ultimately, the described language

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations