Phrase

About: Phrase is a research topic. Over the lifetime, 12580 publications have been published within this topic receiving 317823 citations. The topic is also known as: syntagma & phrases.

Thinking with the Body

Abstract: Thinking with the Body David Kirsh (kirsh@ucsd.edu) Dept of Cognitive Science University of California, San Diego Abstract To explore the question of physical thinking – using the body as an instrument of cognition – we collected extensive video and interview data on the creative process of a noted choreographer and his company as they made a new dance. A striking case of physical thinking is found in the phenomenon of marking. Marking refers to dancing a phrase in a less than complete manner. Dancers mark to save energy. But they also mark to explore the tempo of a phrase, or its movement sequence, or the intention behind it. Because of its representational nature, marking can serve as a vehicle for thought. Importantly, this vehicle is less complex than the version of the same phrase danced ‘full-out’. After providing evidence for distinguishing different types of marking, three ways of understanding marking as a form of thought are considered: marking as a gestural language for encoding aspects of a target movement, marking as a method of priming neural systems involved in the target movement, and marking as a method for improving the precision of mentally projecting aspects of the target. Keywords: Marking; multimodality; thinking, embodied cognition, ethnography. 1. Introduction This paper explores how dancers and choreographers use their bodies to think about dance phrases. My specific focus is a technique called ‘marking’. Marking refers to dancing a phrase in a less than complete manner. See fig. 1 for an example of hand marking, a form that is far smaller than the more typical method of marking that involves modeling a phrase with the whole body. Marking is part of the practice of dance, pervasive in all phases of creation, practice, rehearsal, and reflection. Virtually all English speaking dancers know the term, though few, if any, scholarly articles exist that describe the process or give instructions on how to do it. 1 When dancers mark a phrase, they use their body’s movement and form as a representational vehicle. They do not recreate the full dance phrase they normally perform; instead, they create a simplified or abstracted version – a model. Dancers mark to save energy, to avoid strenuous movement such as jumps, and sometimes to review or explore specific aspects of a phrase, such as tempo, movement sequence, or underlying intention, without the mental complexity involved in creating the phrase ‘full-out’. Marking is not the only way dancers ‘mentally’ explore phrases. Many imagine themselves performing a phrase. Some of the professional dancers we studied reported visualizing their phrase in bed before going to sleep, others reporting mentally reviewing their phrases while traveling on the tube on their way home. Our evidence suggests that marking, however, gives more insight than mental rehearsal: by physically executing a synoptic version of the whole phrase – by creating a simplified version externally – dancers are able to understand the shape, dynamics, emotion, and spatial elements of a phrase better than through imagination alone. They use marking as an anchor and vehicle for thought. It is this idea – that a body in motion can serve as an anchor and vehicle of thought – that is explored in this paper. It is a highly general claim. It has been said that gesture can facilitate thought, [Golden Meadow 05]; that physically simulating a process can help a thinker understand a process [Collins et al 91], and that mental rehearsal is improved by overt physical movement. [Coffman 90] Why? What extra can physical action or physical structure offer to imagination? The answer, I suggest, is that creating an external structure connected to a thought – whether that external structure be a gesture, dance form, or linguistic structure – is part of an interactive strategy of bootstrapping thought by providing an anchor for mental projection. [Hutchins, 05, Kirsh 09, 10]. Marking a phrase provides the scaffold to mentally project more detailed structure than could otherwise be held in mind. It is part of an interactive strategy for augmenting cognition. By marking, dancers harness their bodies to drive thought deeper than through mental simulation and unaided thinking alone. Hand Marking Fig 1a Fig 1b In Fig 1a an Irish river dancer is caught in mid move. In 1b, the same move is marked using just the hands. River dancing is a type of step dancing where the arms are keep still. Typically, river dancers mark steps and positions using one hand for the movement and the other for the floor. Most marking involves modeling phrases with the whole body, and not just the hands. Search by professional librarians of dance in the UK and US has yet to turn up scholarly articles on the practice of marking.

Word sense disambiguation in queries

Abstract: This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense disambiguation. By comparing these pieces of information associated with the words which form a phrase, it may be possible to assign senses to these words. If the above disambiguation fails, then other query words, if exist, are used, by going through exactly the same process. If the sense of a query word cannot be determined in this manner, then a guess of the sense of the word is made, if the guess has at least 50% chance of being correct. If no sense of the word has 50% or higher chance of being used, then we apply a Web search to assist in the word sense disambiguation process. Experimental results show that our approach has 100% applicability and 90% accuracy on the most recent robust track of TREC collection of 250 queries. We combine this disambiguation algorithm to our retrieval system to examine the effect of word sense disambiguation in text retrieval. Experimental results show that the disambiguation algorithm together with other components of our retrieval system yield a result which is 13.7% above that produced by the same system but without the disambiguation, and 9.2% above that produced by using Lesk's algorithm. Our retrieval effectiveness is 7% better than the best reported result in the literature.

Simple Syntactic and Morphological Processing Can Help English-Hindi Statistical Machine Translation.

Abstract: In this paper, we report our work on incorporating syntactic and morphological information for English to Hindi statistical machine translation Two simple and computationally inexpensive ideas have proven to be surprisingly effective: (i) reordering the English source sentence as per Hindi syntax, and (ii) using the suffixes of Hindi words The former is done by applying simple transformation rules on the English parse tree The latter, by using a simple suffix separation program With only a small amount of bilingual training data and limited tools for Hindi, we achieve reasonable performance and substantial improvements over the baseline phrase-based system Our approach eschews the use of parsing or other sophisticated linguistic tools for the target language (Hindi) making it a useful framework for statistical machine translation from English to Indian languages in general, since such tools are not widely available for Indian languages currently

Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization

Abstract: Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.