This paper reports on the participation of ITC-irst in the Cross Language Evaluation Forum (CLEF) of 2001. ITC-irst has taken part to two tracks: the monolingual retrieval task, and the bilingual retrieval task. In both cases, Italian was chosen as the query language, while English was chosen as the document language of the bilingual task. The employed retrieval engine combines scores computed ...

The proposed United States FY2013 budget requests $769 million dollars for information security initiatives under the Department of Homeland Security. Projected annual spending for information security exceeds $10 billion by 2015. A new method using validated processes to quantitatively identify risk of a successful cyber attack would allow targeted interventions and a more efficient use of fun...

A language model is a description of language. Although grammar has been the prevalent tool in modelling language for a long time, interest has recently shifted towards statistical modelling. This chapter refers to speech recognition experiments, although statistical language models are applicable over a wide-range of applications: machine translation, information retrieval, etc. Statistical mo...

In this paper, we propose a method to construct a tree-annotated corpus, when a certain statistical parsing system exists and no tree-annotated corpus is available as training data. The basic idea of our method is to sequentially annotate plain text inputs with syntactic trees using a parser with a statistical language model, and iteratively retrain the statistical language model over the obtai...

Natural language processing has widely used Statistical based language models to solve disambiguation problems. Over the past decades different techniques regarding POS tagging have been proposed for English, European and East Asian languages. In this paper our focus is POS tagging for Urdu due to the infancy stage of Urdu language based tagging system. We have combined two approaches (Statisti...

For automatic speech recognition, the construction of an adequate language model may be difficult when only a limited amount of training text is available. Previous work has shown that in the case of small training sets statistical language models may outperform grammars on out-of-coverage utterances, while showing comparable performance on incoverage input. In this paper, we compare the perfor...