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From the RFR-SUM model, we can see that the model takes the positional information in context and their RFR value into consideration, then sums up all the RFR values in context. This is similar to what human integrates all his knowledge he has ever learnt to make the decision. Table 3 gives some examples for the sum of RFR values in sentences. The three sentences are very similar, but the RFR-SUM Model can discriminate them easily. Especially in the last sentence, where the context words but ”的” are not occurred in training data, the functional words ”的” can give a strong hint to make the true decision at a very high probability.
1. ”的”’s distribution in sentences with ”黄色” and aligned by ”黄色” Based on the above-mentioned facts, we believe that every word in context makes contribution to WSD in one way or another. Here we define collocation 26 W. Qu et al. as co-occurrence of words in context of target word. We introduce Relative Frequency Ratio (RFR) to evaluate the collocation strength. Let the context with ambiguous word A be: W−k W−(k−1)... W−2 W−1 AW1 W2... W(s−1) Ws (1) where, the negative sign in subscript denotes the word in left context; −k denotes that left context selects k words; s denotes that right context selects s words.
Later version inSelect a pair from Pall based on chunking troduced a beam search to criterion alleviate this problem (BChunk the selected pair into one node c GBI), but it was not enough. Gc := contracted graph of G The recent improved version while termination condition not reached Cl-GBI extends it to emP := P ∪ GBI(Gc ) ploy pseudo-chunking which return P is called chunkingless chunking, enabling to extract overFig. 9. Algorithm of GBI lapping subgraphs . In Cl-GBI, the selected Pseudo-node pairs are registered as new 1 1 1 1 PseudoPseudonodes and assigned new la- 6 1 1 3 3 3 3 Chunking 6 Chunking 2 2 2 2 4 bels but are never chunked 4 4 3 4 3 1 1 and the graphs are never 3 3 6 6 5 5 2 2 “contracted” nor copied into respective states as in B1 2 1 3 3 4 1 3 2 1 2 1 2 3 1 GBI.
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