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MaxEnt: Elegant Data Structures for NLP Results Presentation

This project presentation covers the use of Maximum Entropy (MaxEnt) model in Natural Language Processing (NLP) with a focus on simple feature templates and data structures for efficient storage and access. The team discusses both positive and negative aspects of their approach, highlighting decent results and areas for improvement. Additionally, the presentation includes a Bagging Tie function and a Voting combination method to refine tagging results. The team showcases procedures for creating Bootstrap samples and selecting the best tag for each word.

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MaxEnt: Elegant Data Structures for NLP Results Presentation

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Presentation Transcript

  1. Lin572 Advanced Statistic Methods in NLP Project Presentation Team Members: Anna Tinnemore Gabriel Neer Yow-Ren Chiang

  2. PART 3 MaxEnt (yipee!)

  3. The Good Stuff: • Simple feature templates and extraction • Elegant data structures for storage and easy access • Pretty good results!

  4. The Bad Stuff: • Hmmm. . . .

  5. Features • A few short loops collected the most relevant context features • No long-winded feature templates • Easy-access hashes

  6. Decent Results • Mid-nineties increasing with the size of the training data • Result

  7. PART 4 Task 2 Bagging

  8. Tie Function • use Tie::File; • use Fcntl; • for my $bag_num (1 .. $B) { # The Nth bag from file foo.txt becomes foo.txtbagN, etc. my $bag_name = "$file_name-bag$bag_num"; open (BAG, ">$bag_name") or die "Can't open $bag_name for writing: $!"; for (@lines) { # Pick random line of file. my $line = $lines[ rand @lines ]; print BAG "$line\n"; # Output to the bag. } }

  9. Combination • VOTING!!

  10. Step 1: • # Loop through file and remember words. Keep them grouped by sentence. while (<FILE>) { foreach (@word_tags) { my @wordtag = split /\//; push (@words, ($wordtag[0])); } push (@sentences, (\@words)); }

  11. Step 2: • # Go through file and for each word, increase the count of its tag for (@ARGV) { my $tag_index = 0; while (<FILE>) { foreach (@word_tags) { my @wordtag = split /\//; my $tag = $wordtag[1]; $tags[$tag_index]->{$tag}++; $tag_index++; } } }

  12. Step 3: • # Go through the sentences and print out each word/tag pair. my $tag_index = 0; foreach my $sent (@sentences) { foreach my $word (@$sent) { my $tag = max_tag($tags[$tag_index]); $tag_index++; print "$word/$tag "; } print "\n"; }

  13. Finding the “Best Tag” • # Find the tag with the highest count. sub max_tag { my $tag_hash = shift; (my $tag) = keys %$tag_hash; my $tag_count = $tag_hash->{$tag}; foreach (keys %$tag_hash) { if ($tag_hash->{$_} > $tag_count) { $tag = $_; $tag_count = $tag_hash->{$tag} } } return $tag; }

  14. Procedure • Creating Bootstrap samples • Treating the file as an array for lines. • N random array indices are selected and each corresponding line is output to a file • Combine_tool.pl • opens the file corresponding to its first argument • reads in all words, aggregated by sentence • An array of tag hashes is created. • For each file in its arg list, opens that file and reads the tags sequentially • The hash item corresponding to the tag in the appropriate index of the tag area is incremented • For each index, the hash label with the highest count is chosen as the correct tag • Re-associate the tags with their words • Print out the word/tag pairs

  15. Result

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