Language Learning

A Review of Statistical Language Learning

AI Magazine

Several factors have led to the increase in interest in this field, which is heavily influenced by techniques from speech processing. One major factor is the recent availability of large online text collections. Another is a disillusionment with traditional AIbased approaches to parsing and natural language processing (NLP). Charniak is recognized as a distinguished contributor to what he calls traditional AI NLP, which is why it is all the more significant that in the Preface, when speaking of his recent transition to the statistical approach, he writes … few, if any, consider the traditional study of language from an artificial-intelligence point of view a "hot" area of research. A great deal of work is still done on specific NLP problems, from grammatical issues to stylistic considerations, but for me at least it is increasingly hard to believe that it will shed light on broader problems, since it has steadfastly refused to do so in the past.

Recognising sign language signs from glove sensor data


The data consists of a sample of Australian Sign Language signs performed by volunteers. There are 95 unique signs, each recorded 27 times on different days. The data was recorded using two Fifth Dimension Technologies (5DT) gloves (one for each hand) and two Ascension Flock-of-Birds magnetic position trackers. Together, this produced 22 channels of data, 11 for each hand. These channels included x, y and z position, roll, pitch and yaw movements and finger bend measurements for each finger.