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Implemented two graph partition algorithms

Implemented two graph partition algorithms. 1. Kernighan/Lin Algorithm. Produce bi-partition to undirected weighted graph (Min-cut) ‏. Input: $network, a Clair::Network object. Usage: use Clair::Network::KernighanLin; my $KL = new KernighanLin($net);

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Implemented two graph partition algorithms

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  1. Implemented two graph partition algorithms 1. Kernighan/Lin Algorithm Produce bi-partition to undirected weighted graph (Min-cut)‏ Input: $network, a Clair::Network object Usage: use Clair::Network::KernighanLin; my $KL = new KernighanLin($net); $graphPartition = $KL->generatePartition(); Output: $graphPartition, a hash object: $graphPartition == { $node1 => 0, $node2 => 1, .... };

  2. Implemented two graph partition algorithms 2. Girvan/Newman Algorithm produce hierarchical clustering (multiple partitions) for unweighted, undirected graph Input: $network, a Clair::Network object Usage: use Clair::Network::GirvanNewman; my $GN = new GirvanNewman$net); $graphPartition = $GN->partition(); Output: $graphPartition a hash object: $graphPartiton == { $node1 = 0|1|1|1|1|1|1|1|1|2|2|2|2|2|2|2|2|3|3|3|4|4|4|4|7|7|8|8|8|8|9|9|10|10|10|10|11|11|11|4|4|4|4|5|5|6|6|6|6|6|7|7|8|8|8|8|8|8|8|8|9|9|9|9|9|9|9|20|21, $node2 = 0|1|1|1|1|1|1|1|1|2|2|2|2|2|2|2|2|1|1|1|2|2|2|2|5|5|6|6|6|6|7|7|8|8|8|8|9|9|9|3|3|3|3|4|4|4|4|4|4|4|5|5|6|6|6|6|6|6|6|6|7|7|7|7|7|7|7|6|7, .... }

  3. Implemented a dependency parser produce non-projective, unlabeled dependency tree Input: the dow fell 22.6 % on black monday . DT NNP VBD CD NN IN JJ NNP . DEP NP-SBJ ROOT DEP NP PP DEP NP DEP 2 3 0 5 3 3 8 6 3 the sentence POS (manually added)‏ Dependency Label (for reference)‏ Usage: perl MSTParser.pl $inputData perl MSTParser.pl model $inputData dependency parent (for reference)‏ Output: out.txt the dow fell 22.6 % on black monday . DT NNP VBD CD NN IN JJ NNP . 2 3 0 5 3 3 8 6 3 dependency parent (parsed result)‏

  4. Result Slightly off: substitute 11.11 to <num>

  5. Some Observations 1. extract features related to the edge John saw NNP VBD <Head> NNP VBD NNP 2. Compare features with exist features and their weights John saw 0 NNP VBD 15 <Head> NNP VBD NNP 5 3. Add up the weights Weight(John -> saw) = 20 Most time consuming part: calculating the edge weights.

  6. Questions: 1. Do we really need a complete graph? 2. Do we really need so many features ? 1APT4=<root-POS> MID MID:-0.03632653020122016 1APT4=<root-POS> MID MID:-0.03632653020122016 1APT2=<root-POS> MID UH:0 1APT1=<root-POS> MID UH:0 1APT=<root-POS> MID MID UH:0 APT4=<root-POS> MID MID&RA&0:-0.03632653020122016 APT4=<root-POS> MID MID&RA&0:-0.03632653020122016 APT2=<root-POS> MID UH&RA&0:0 APT1=<root-POS> MID UH&RA&0:0 APT=<root-POS> MID MID UH&RA&0:0 X1PT4=STR < ,:-0.025903170059057394 X1PT4=STR < ,:-0.025903170059057394 X1PT2=STR < U:0 X1PT1=< U ,:0 X1PT=STR < U ,:0 XPT4=STR < ,&RA&0:0 XPT3=STR U ,&RA&0:0 XPT2=STR < U&RA&0:0 XPT1=< U ,&RA&0:0 XPT=STR < U ,&RA&0:0 1PT4=STR <root-POS> ,:-0.025903170059057394 1PT4=STR <root-POS> ,:-0.025903170059057394 1PT2=STR <root-POS> UH:0 1PT1=<root-POS> UH ,:0 1PT=STR <root-POS> UH ,:0 PT4=STR <root-POS> ,&RA&0:0 PT3=STR UH ,&RA&0:0 PT2=STR <root-POS> UH&RA&0:0 PT1=<root-POS> UH ,&RA&0:0 PT=STR <root-POS> UH ,&RA&0:0 X1APT4=< MID MID:-0.03632653020122016 X1APT4=< MID MID:-0.03632653020122016 X1APT2=< MID U:0 X1APT1=< MID U:0 X1APT=< MID MID U:0 XAPT4=< MID MID&RA&0:-0.03632653020122016 XAPT4=< MID MID&RA&0:-0.03632653020122016 XAPT2=< MID U&RA&0:0 XAPT1=< MID U&RA&0:0 XAPT=< MID MID U&RA&0:0

  7. 3. Why the sentence correction rate is so low? ! ! 4. Do we really need “online large margin training”? Maybe just feature frequency?

  8. Thank you!

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