Parsing

By Ralph Grishman. 1981

One reason for the wide variety of views on many subjects in computational linguistics (such as parsing) is the diversity of objectives which lead people to do research in this area. Some researchers are motivated primarily by potential applications the development of natural language interfaces for computer systems. Others are primarily concerned with the psychological processes which underlie human language, and view the computer as a tool for modeling and thus improving our understanding of these processes. Since, as is often observed, man is our best example of a natural language processor, these two groups do have a strong commonality of research interest. Nonetheless, their divergence of objective must lead to differences in the way they regard the component processes of natural language understanding. (If when human processing is better understood it is recognized that the simulation of human processes is not the most effective way of constructing a natural language interface, there may even be a deliberate divergence in the processes themselves.) My work, and this position paper, reflect an applications orientation; those with different research objectives will come to quite different conclusions.

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Citations in the corpus

Cross-View Semantic Segmentation for Sensing Surroundings (2019, methodology): “Recent progress in deep learning, including deep convolutional neural networks, has enabled machine vision to segment an image precisely into meaningful regions and objects [47, 25].”
MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic (2014, unknown): “ATB_BWFORM_EVAL This is the same as ATB_BWFORM , except that the tokens are output in Buckwalter, and tokens of the same word are connected to each other with underscores.”