Sentence examples for methods for word from inspiring English sources
Discriminative methods for word spotting have been recently investigated in [25 27].
In " Evaluating word representation features in biomedical named entity recognition tasks" by B. Tang et al., the authors present a comparative analysis of three different methods for word representation in recognition of named entities from biomedical literature.
This formula represents the weight calculation method for word t in document d.
The most common method for word extraction uses a sliding window of a fixed size.
One direct method for word similarity calculation is to find the synonymy set of each word so as to detect the common synonyms between the two words.
We show how different methods for generating word embeddings and additional features differ in accuracy.
These characteristics are important when selecting methods for automatic word alignment.
Although the difference in performance gain could be due to different methods for generating word representation features, we would think it is more related to the baseline performance.
This paper illustrates two kinds of semantic input method for Chinese word senses such as Word-based word sense input and Sentence-based word sense input.
In Experiment 2 (N = 27), we used two methods for teaching words: one requiring more effort on the part of children (questions) and one requiring less effort ("hotspots" that provide definitions).
SENNA not only proposed the method for building word embedding but also solved the natural language processing tasks (POS, Chunking, NER, SRL) from the perspective of neural network language model system.
