An option is a nancial contract whose value depends on that of an underlying asset such as a company stock. The Black-Scholes model for option pricing, published in 1973, revolutionized the nancial industry by introducing a no-arbitrage paradigm for valuing uncertainty and hedging against risk. This simple model assumes that the underlying stock price follows a stochastic Brownian motion proces...

People -both residents and visitors- have certain images of streets alleys Kagurazaka. For example, Kagurazaka Street, the main street area, is vibrant with many shops restaurants. The historic are narrow stone-paved, surrounded by black wood-board wall, quiet. However, quantity analysis streetscape in Kagurazaka, from pedestrian views, not been done. Such study important to recognize share cur...

This paper presents the English valency lexicon EngValLex, built within the Functional Generative Description framework. The form of the lexicon, as well as the process of its semi-automatic creation is described. The lexicon describes valency for verbs and also includes links to other lexical sources, namely PropBank. Basic statistics about the lexicon are given. The lexicon will be later used...

We present an algorithm for generating referring expressions in open domains. Existing algorithms work at the semantic level and assume the availability of a classification for attributes, which is only feasible for restricted domains. Our alternative works at the realisation level, relies on WordNet synonym and antonym sets, and gives equivalent results on the examples cited in the literature ...

Most attempts to train part-of-speech taggers on a mixture of labeled and unlabeled data have failed. In this work stacked learning is used to reduce tagging to a classification task. This simplifies semisupervised training considerably. Our prefered semi-supervised method combines tri-training (Li and Zhou, 2005) and disagreement-based co-training. On the Wall Street Journal, we obtain an erro...

Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other...

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as SPATTER (Magerman 95; Jelinek et al. 94), which ...

We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms. Our approach, called ‘iterated reranking’ (IR), starts with dependency trees generated by an unsupervised parser, and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees. Ou...

We study self-training with products of latent variable grammars in this paper. We show that increasing the quality of the automatically parsed data used for self-training gives higher accuracy self-trained grammars. Our generative self-trained grammars reach F scores of 91.6 on the WSJ test set and surpass even discriminative reranking systems without selftraining. Additionally, we show that m...

In recent decades, skyscrapers, as represented by the Burj Khalifa in Dubai and Shanghai Tower in Shanghai, have been built due to the improvements of construction technologies. Even in such newfangled skyscrapers, the façades are generally cleaned by humans. Wall climbing robots, which are capable of climbing up vertical surfaces, ceilings and roofs, are expected to replace the manual workforc...