void ExtractTask::saveHieroAlignment( int startT, int endT, int startS, int endS , const WordIndex &indexS, const WordIndex &indexT, HoleCollection &holeColl, ExtractedRule &rule) { // print alignment of words for(int ti=startT; ti<=endT; ti++) { WordIndex::const_iterator p = indexT.find(ti); if (p != indexT.end()) { // does word still exist? for(unsigned int i=0; i<m_sentence.alignedToT[ti].size(); i++) { int si = m_sentence.alignedToT[ti][i]; std::string sourceSymbolIndex = IntToString(indexS.find(si)->second); std::string targetSymbolIndex = IntToString(p->second); rule.alignment += sourceSymbolIndex + "-" + targetSymbolIndex + " "; if (! m_options.onlyDirectFlag) rule.alignmentInv += targetSymbolIndex + "-" + sourceSymbolIndex + " "; } } } // print alignment of non terminals HoleList::const_iterator iterHole; for (iterHole = holeColl.GetHoles().begin(); iterHole != holeColl.GetHoles().end(); ++iterHole) { const Hole &hole = *iterHole; std::string sourceSymbolIndex = IntToString(hole.GetPos(0)); std::string targetSymbolIndex = IntToString(hole.GetPos(1)); rule.alignment += sourceSymbolIndex + "-" + targetSymbolIndex + " "; if (!m_options.onlyDirectFlag) rule.alignmentInv += targetSymbolIndex + "-" + sourceSymbolIndex + " "; } rule.alignment.erase(rule.alignment.size()-1); if (!m_options.onlyDirectFlag) { rule.alignmentInv.erase(rule.alignmentInv.size()-1); } }
void ApParser::train(SentenceReader* sentenceReader, char const* modelFile) { WordIndex labelIndex; vector<string> labels; vector<string> predLabels; // collect events list<Tanl::Classifier::Event*> events; WordCounts predCount; // count predicate occurrences int evCount = 0; Tanl::Classifier::PID pID = 1; // leave 0 for bias // create inverted index of predicate names // used to create vector of pIDs EventStream eventStream(sentenceReader, &info); while (eventStream.hasNext()) { Tanl::Classifier::Event* ev = eventStream.next(); events.push_back(ev); evCount++; // count them explicitly, since size() is costly if (config.verbose) { if (evCount % 10000 == 0) cerr << '+' << flush; else if (evCount % 1000 == 0) cerr << '.' << flush; } vector<string>& ec = ev->features; // ec = {p1, ... , pn} for (unsigned j = 0; j < ec.size(); j++) { string& pred = ec[j]; // decide whether to retain it (# occurrences > cutoff) if (predIndex.find(pred.c_str()) == predIndex.end()) { // not yet among those retained WordCounts::iterator wcit = predCount.find(pred); // increment # of occurrences int count; if (wcit == predCount.end()) count = predCount[pred] = 1; else count = ++wcit->second; if (count >= config.featureCutoff) { predLabels.push_back(pred); // accept it into predLabels predIndex[pred.c_str()] = pID++; predCount.erase(pred); } } } } if (config.verbose) cerr << endl; // build cases Cases cases; cases.reserve(evCount); int n = 0; Tanl::Classifier::ClassID oID = 0; while (!events.empty()) { Tanl::Classifier::Event* ev = events.front(); events.pop_front(); cases.push_back(Case()); X& x = cases[n].first; // features // add features vector<string>& ec = ev->features; // ec = {p1, ... , pn} char const* c = ev->className.c_str(); for (unsigned j = 0; j < ec.size(); j++) { string& pred = ec[j]; WordIndex::const_iterator pit = predIndex.find(pred.c_str()); if (pit != predIndex.end()) { x.push_back(pit->second); } } if (x.size()) { if (labelIndex.find(c) == labelIndex.end()) { labelIndex[c] = oID++; labels.push_back(c); } cases[n].second = labelIndex[c]; n++; if (config.verbose) { if (n % 10000 == 0) cerr << '+' << flush; else if (n % 1000 == 0) cerr << '.' << flush; } x.push_back(0); // bias } delete ev; } cases.resize(n); if (config.verbose) cerr << endl; int predSize = predLabels.size(); predSize++; // bias APSV ap(labels.size(), predSize); ofstream ofs(modelFile, ios::binary | ios::trunc); // dump configuration settings config.writeHeader(ofs); // dump labels ofs << labels.size() << endl; FOR_EACH (vector<string>, labels, pit) ofs << *pit << endl; // dump predLabels ofs << predLabels.size() << endl; FOR_EACH (vector<string>, predLabels, pit) ofs << *pit << endl; // free memory predIndex.clear(); WordIndex().swap(predIndex); // STL map do not deallocate. resize(0) has no effect labelIndex.clear(); WordIndex().swap(labelIndex); // clear memory for unfrequent entities info.clearRareEntities(); // perform training ap.train(cases, iter); // dump parameters ap.save(ofs); // dump global info info.save(ofs); }