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#include "ff_csplit.h"
#include <set>
#include <cstring>
#include "Vocab.h"
#include "Ngram.h"
#include "sentence_metadata.h"
#include "lattice.h"
#include "tdict.h"
#include "freqdict.h"
#include "filelib.h"
#include "stringlib.h"
#include "tdict.h"
using namespace std;
struct BasicCSplitFeaturesImpl {
BasicCSplitFeaturesImpl(const string& param) :
word_count_(FD::Convert("WordCount")),
letters_sq_(FD::Convert("LettersSq")),
letters_sqrt_(FD::Convert("LettersSqrt")),
in_dict_(FD::Convert("InDict")),
short_(FD::Convert("Short")),
long_(FD::Convert("Long")),
oov_(FD::Convert("OOV")),
short_range_(FD::Convert("ShortRange")),
high_freq_(FD::Convert("HighFreq")),
med_freq_(FD::Convert("MedFreq")),
freq_(FD::Convert("Freq")),
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) {
cerr << "Expected: freqdict.txt [badwords.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));
}
}
}
void TraversalFeaturesImpl(const Hypergraph::Edge& edge,
SparseVector<double>* features) const;
const int word_count_;
const int letters_sq_;
const int letters_sqrt_;
const int in_dict_;
const int short_;
const int long_;
const int oov_;
const int short_range_;
const int high_freq_;
const int med_freq_;
const int freq_;
const int fl1_;
const int fl2_;
const int bad_;
FreqDict freq_dict_;
set<WordID> bad_words_;
};
BasicCSplitFeatures::BasicCSplitFeatures(const string& param) :
pimpl_(new BasicCSplitFeaturesImpl(param)) {}
void BasicCSplitFeaturesImpl::TraversalFeaturesImpl(
const Hypergraph::Edge& edge,
SparseVector<double>* features) const {
features->set_value(word_count_, 1.0);
features->set_value(letters_sq_, (edge.j_ - edge.i_) * (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);
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(freq_, freq);
features->set_value(in_dict_, 1.0);
} else {
features->set_value(oov_, 1.0);
freq = 99.0f;
}
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::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, features);
}
struct ReverseCharLMCSplitFeatureImpl {
ReverseCharLMCSplitFeatureImpl(const string& param) :
order_(5),
vocab_(*TD::dict_),
ngram_(vocab_, order_) {
kBOS = vocab_.getIndex("<s>");
kEOS = vocab_.getIndex("</s>");
File file(param.c_str(), "r", 0);
assert(file);
cerr << "Reading " << order_ << "-gram LM from " << param << endl;
ngram_.read(file);
}
double LeftPhonotacticProb(const Lattice& inword, const int start) {
const int end = inword.size();
for (int i = 0; i < order_; ++i)
sc[i] = kBOS;
int sp = min(end - start, order_ - 1);
// cerr << "[" << start << "," << sp << "]\n";
int ci = (order_ - sp - 1);
int wi = start;
while (sp > 0) {
sc[ci] = inword[wi][0].label;
// cerr << " CHAR: " << TD::Convert(sc[ci]) << " ci=" << ci << endl;
++wi;
++ci;
--sp;
}
// cerr << " END ci=" << ci << endl;
sc[ci] = Vocab_None;
const double startprob = ngram_.wordProb(kEOS, sc);
// cerr << " PROB=" << startprob << endl;
return startprob;
}
private:
const int order_;
Vocab& vocab_;
VocabIndex kBOS;
VocabIndex kEOS;
Ngram ngram_;
VocabIndex sc[80];
};
ReverseCharLMCSplitFeature::ReverseCharLMCSplitFeature(const string& param) :
pimpl_(new ReverseCharLMCSplitFeatureImpl(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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