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#include "ff_csplit.h"
#include <set>
#include <cstring>
#include "klm/lm/model.hh"
#include "hg.h"
#include "sentence_metadata.h"
#include "lattice.h"
#include "tdict.h"
#include "freqdict.h"
#include "filelib.h"
#include "stringlib.h"
#include "tdict.h"
#ifndef HAVE_OLD_CPP
# include <unordered_set>
#else
# include <tr1/unordered_set>
namespace std { using std::tr1::unordered_set; }
#endif
using namespace std;
struct BasicCSplitFeaturesImpl {
BasicCSplitFeaturesImpl(const string& param) :
word_count_(FD::Convert("WordCount")),
letters_sq_(FD::Convert("LettersSq")),
letters_log_(FD::Convert("LettersLog")),
letters_sqrt_(FD::Convert("LettersSqrt")),
in_dict_(FD::Convert("InDict")),
in_dict_sub_word_(FD::Convert("InDictSubWord")),
short_(FD::Convert("Short")),
long_(FD::Convert("Long")),
oov_(FD::Convert("OOV")),
oov_sub_word_(FD::Convert("OOVSubWord")),
short_range_(FD::Convert("ShortRange")),
high_freq_(FD::Convert("HighFreq")),
med_freq_(FD::Convert("MedFreq")),
logfreq_(FD::Convert("LogFreq")),
loglogfreq_(FD::Convert("LogLogFreq")),
in_dict_full_word_(FD::Convert("InDictFullWord")),
fl1_(FD::Convert("FreqLen1")),
fl2_(FD::Convert("FreqLen2")),
bad_(FD::Convert("Bad")) {
vector<string> argv;
int argc = SplitOnWhitespace(param, &argv);
if (argc != 1 && argc != 2 && argc != 3) {
cerr << "Expected: freqdict.txt [badwords.txt] [sensitvewords.txt]\n";
abort();
}
freq_dict_.Load(argv[0]);
if (argc == 2) {
ReadFile rf(argv[1]);
istream& in = *rf.stream();
while(in) {
string badword;
in >> badword;
if (badword.empty()) continue;
bad_words_.insert(TD::Convert(badword));
}
}
if (argc == 3) {
ReadFile rf(argv[2]);
istream& in = *rf.stream();
string line;
while(getline(in, line)) {
special_feats_[TD::Convert(line)] = FD::Convert("CS:"+line);
}
}
}
void TraversalFeaturesImpl(const Hypergraph::Edge& edge,
const int src_word_size,
SparseVector<double>* features) const;
const int word_count_;
const int letters_sq_;
const int letters_log_;
const int letters_sqrt_;
const int in_dict_;
const int in_dict_sub_word_;
const int short_;
const int long_;
const int oov_;
const int oov_sub_word_;
const int short_range_;
const int high_freq_;
const int med_freq_;
const int logfreq_;
const int loglogfreq_;
const int in_dict_full_word_;
const int fl1_;
const int fl2_;
const int bad_;
FreqDict<float> freq_dict_;
set<WordID> bad_words_;
unordered_map<WordID, int> special_feats_;
};
BasicCSplitFeatures::BasicCSplitFeatures(const string& param) :
pimpl_(new BasicCSplitFeaturesImpl(param)) {}
void BasicCSplitFeaturesImpl::TraversalFeaturesImpl(
const Hypergraph::Edge& edge,
const int src_word_length,
SparseVector<double>* features) const {
const bool subword = (edge.i_ > 0) || (edge.j_ < src_word_length);
string len_bias = "LenBias_0";
int swlen = log(src_word_length) / log(1.69);
if (swlen > 9) swlen = 9;
len_bias[8] += swlen;
int fid_len_bias_ = FD::Convert(len_bias);
features->set_value(fid_len_bias_, 1.0);
features->set_value(word_count_, 1.0);
features->set_value(letters_sq_, (edge.j_ - edge.i_) * (edge.j_ - edge.i_));
features->set_value(letters_log_, log(edge.j_ - edge.i_));
features->set_value(letters_sqrt_, sqrt(edge.j_ - edge.i_));
const WordID word = edge.rule_->e_[1];
const char* sword = TD::Convert(word).c_str();
const int len = strlen(sword);
int cur = 0;
int chars = 0;
while(cur < len) {
cur += UTF8Len(sword[cur]);
++chars;
}
// these are corrections that attempt to make chars
// more like a phoneme count than a letter count, they
// are only really meaningful for german and should
// probably be gotten rid of
bool has_sch = strstr(sword, "sch");
bool has_ch = (!has_sch && strstr(sword, "ch"));
bool has_ie = strstr(sword, "ie");
bool has_zw = strstr(sword, "zw");
if (has_sch) chars -= 2;
if (has_ch) --chars;
if (has_ie) --chars;
if (has_zw) --chars;
float freq = freq_dict_.LookUp(word);
if (freq) {
features->set_value(logfreq_, freq);
features->set_value(loglogfreq_, log(freq) / log(1.69));
features->set_value(in_dict_, 1.0);
if (subword) features->set_value(in_dict_sub_word_, 1.0);
} else {
if (!subword) features->set_value(in_dict_full_word_, 1.0);
features->set_value(oov_, 1.0);
if (subword) features->set_value(oov_sub_word_, 1.0);
freq = 99.0f;
}
const unordered_map<WordID, int>::const_iterator it = special_feats_.find(word);
if (it != special_feats_.end())
features->set_value(it->second, 1.0);
if (bad_words_.count(word) != 0)
features->set_value(bad_, 1.0);
if (chars < 5)
features->set_value(short_, 1.0);
if (chars > 10)
features->set_value(long_, 1.0);
if (freq < 7.0f)
features->set_value(high_freq_, 1.0);
if (freq > 8.0f && freq < 10.f)
features->set_value(med_freq_, 1.0);
if (freq < 10.0f && chars < 5)
features->set_value(short_range_, 1.0);
// i don't understand these features, but they really help!
features->set_value(fl1_, sqrt(chars * freq));
features->set_value(fl2_, freq / chars);
}
void BasicCSplitFeatures::PrepareForInput(const SentenceMetadata& smeta) {}
void BasicCSplitFeatures::TraversalFeaturesImpl(
const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* out_context) const {
(void) smeta;
(void) ant_contexts;
(void) out_context;
(void) estimated_features;
if (edge.Arity() == 0) return;
if (edge.rule_->EWords() != 1) return;
pimpl_->TraversalFeaturesImpl(edge, smeta.GetSourceLattice().size(), features);
}
namespace {
struct CSVMapper : public lm::EnumerateVocab {
CSVMapper(vector<lm::WordIndex>* out) : out_(out), kLM_UNKNOWN_TOKEN(0) { out_->clear(); }
void Add(lm::WordIndex index, const StringPiece &str) {
const WordID cdec_id = TD::Convert(str.as_string());
if (cdec_id >= out_->size())
out_->resize(cdec_id + 1, kLM_UNKNOWN_TOKEN);
(*out_)[cdec_id] = index;
}
vector<lm::WordIndex>* out_;
const lm::WordIndex kLM_UNKNOWN_TOKEN;
};
}
template<class Model>
struct ReverseCharLMCSplitFeatureImpl {
ReverseCharLMCSplitFeatureImpl(const string& param) {
CSVMapper vm(&cdec2klm_map_);
lm::ngram::Config conf;
conf.enumerate_vocab = &vm;
cerr << "Reading character LM from " << param << endl;
ngram_ = new Model(param.c_str(), conf);
order_ = ngram_->Order();
kEOS = MapWord(TD::Convert("</s>"));
assert(kEOS > 0);
}
lm::WordIndex MapWord(const WordID w) const {
if (w < cdec2klm_map_.size()) return cdec2klm_map_[w];
return 0;
}
double LeftPhonotacticProb(const Lattice& inword, const int start) {
const int end = inword.size();
lm::ngram::State state = ngram_->BeginSentenceState();
int sp = min(end - start, order_ - 1);
// cerr << "[" << start << "," << sp << "]\n";
int wi = start + sp - 1;
while (sp > 0) {
const lm::ngram::State scopy(state);
ngram_->Score(scopy, MapWord(inword[wi][0].label), state);
--wi;
--sp;
}
const lm::ngram::State scopy(state);
const double startprob = ngram_->Score(scopy, kEOS, state);
return startprob;
}
private:
Model* ngram_;
int order_;
vector<lm::WordIndex> cdec2klm_map_;
lm::WordIndex kEOS;
};
ReverseCharLMCSplitFeature::ReverseCharLMCSplitFeature(const string& param) :
pimpl_(new ReverseCharLMCSplitFeatureImpl<lm::ngram::ProbingModel>(param)),
fid_(FD::Convert("RevCharLM")) {}
void ReverseCharLMCSplitFeature::TraversalFeaturesImpl(
const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const std::vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* out_context) const {
(void) ant_contexts;
(void) estimated_features;
(void) out_context;
if (edge.Arity() != 1) return;
if (edge.rule_->EWords() != 1) return;
const double lpp = pimpl_->LeftPhonotacticProb(smeta.GetSourceLattice(), edge.i_);
features->set_value(fid_, lpp);
}
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