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#include "ff_parse_match.h"
#include <sstream>
#include <stack>
#include <string>
#include "sentence_metadata.h"
#include "array2d.h"
#include "filelib.h"
using namespace std;
// implements the parse match features as described in Vilar et al. (2008)
// source trees must be represented in Penn Treebank format, e.g.
// (S (NP John) (VP (V left)))
//
// Annotate source sentences with <seg id="..." grammar="..." src_tree="(S ...)">...</seg>"
// Note: You need to escape quite a lot of stuff in all your models!
//
struct ParseMatchFeaturesImpl {
ParseMatchFeaturesImpl(const string& param) {
if (param.compare("") != 0) {
char score_param = (char) param[0];
switch(score_param) {
case 'b':
scoring_method = 0;
break;
case 'l':
scoring_method = 1;
break;
case 'e':
scoring_method = 2;
break;
case 'r':
scoring_method = 3;
break;
default:
scoring_method = 0;
}
}
else {
scoring_method = 0;
}
}
void InitializeGrids(const string& tree, unsigned src_len) {
assert(tree.size() > 0);
fids_ef.clear();
src_tree.clear();
fids_ef.resize(src_len, src_len + 1);
src_tree.resize(src_len, src_len + 1, TD::Convert("XX"));
ParseTreeString(tree, src_len);
}
void ParseTreeString(const string& tree, unsigned src_len) {
//cerr << "TREE: " << tree << endl;
src_sent_len = src_len;
stack<pair<int, WordID> > stk; // first = i, second = category
pair<int, WordID> cur_cat; cur_cat.first = -1;
unsigned i = 0;
unsigned p = 0;
while(p < tree.size()) {
const char cur = tree[p];
if (cur == '(') {
stk.push(cur_cat);
++p;
unsigned k = p + 1;
while (k < tree.size() && tree[k] != ' ') { ++k; }
cur_cat.first = i;
cur_cat.second = TD::Convert(tree.substr(p, k - p));
// cerr << "NT: '" << tree.substr(p, k-p) << "' (i=" << i << ")\n";
p = k + 1;
} else if (cur == ')') {
unsigned k = p;
while (k < tree.size() && tree[k] == ')') { ++k; }
const unsigned num_closes = k - p;
for (unsigned ci = 0; ci < num_closes; ++ci) {
// cur_cat.second spans from cur_cat.first to i
// cerr << TD::Convert(cur_cat.second) << " from " << cur_cat.first << " to " << i << endl;
// NOTE: unary rule chains end up being labeled with the top-most category
src_tree(cur_cat.first, i) = cur_cat.second;
cur_cat = stk.top();
stk.pop();
}
p = k;
while (p < tree.size() && (tree[p] == ' ' || tree[p] == '\t')) { ++p; }
} else if (cur == ' ' || cur == '\t') {
cerr << "Unexpected whitespace in: " << tree << endl;
abort();
} else { // terminal symbol
unsigned k = p + 1;
do {
while (k < tree.size() && tree[k] != ')' && tree[k] != ' ') { ++k; }
// cerr << "TERM: '" << tree.substr(p, k-p) << "' (i=" << i << ")\n";
++i;
assert(i <= src_len);
while (k < tree.size() && tree[k] == ' ') { ++k; }
p = k;
} while (p < tree.size() && tree[p] != ')');
}
//cerr << "i=" << i << " src_len=" << src_len << endl;
}
//cerr << "i=" << i << " src_len=" << src_len << endl;
assert(i == src_len); // make sure tree specified in src_tree is
// the same length as the source sentence
}
int FireFeatures(const TRule& rule, const int i, const int j, int* ants, SparseVector<double>* feats) {
//cerr << "fire features: " << rule.AsString() << " for " << i << "," << j << endl;
//cerr << rule << endl;
//cerr << "span: " << i << " " << j << endl;
const WordID lhs = src_tree(i,j);
int fid_ef = FD::Convert("PM");
int min_dist; // minimal distance to next syntactic constituent of this rule's LHS
int summed_min_dists; // minimal distances of LHS and NTs summed up
if (TD::Convert(lhs).compare("XX") != 0)
min_dist= 0;
// compute the distance to the next syntactical constituent
else {
int ok = 0;
for (unsigned int k = 1; k < (j - i); k++) {
min_dist = k;
for (unsigned int l = 0; l <= k; l++) {
// check if adding k words to the rule span will
// lead to a syntactical constituent
int l_add = i-l;
int r_add = j+(k-l);
//cerr << "Adding: " << l_add << " " << r_add << endl;
if ((l_add < src_tree.width() && r_add < src_tree.height()) && (TD::Convert(src_tree(l_add, r_add)).compare("XX") != 0)) {
//cerr << TD::Convert(src_tree(i-l,j+(k-l))) << endl;
//cerr << "span_add: " << l_add << " " << r_add << endl;
ok = 1;
break;
}
// check if removing k words from the rule span will
// lead to a syntactical constituent
else {
//cerr << "Hilfe...!" << endl;
int l_rem= i+l;
int r_rem = j-(k-l);
//cerr << "Removing: " << l_rem << " " << r_rem << endl;
if ((l_rem < src_tree.width() && r_rem < src_tree.height()) && TD::Convert(src_tree(l_rem, r_rem)).compare("XX") != 0) {
//cerr << TD::Convert(src_tree(i+l,j-(k-l))) << endl;
//cerr << "span_rem: " << l_rem << " " << r_rem << endl;
ok = 1;
break;
}
}
}
if (ok) break;
}
}
summed_min_dists = min_dist;
//cerr << min_dist << endl;
unsigned ntc = 0;
for (unsigned k = 0; k < rule.f_.size(); ++k) {
int fj = rule.f_[k];
if (fj <= 0)
summed_min_dists += ants[ntc++];
}
switch(scoring_method) {
case 0:
// binary scoring
feats->set_value(fid_ef, (summed_min_dists == 0));
break;
// CHECK: for the remaining scoring methods, the question remains if
// min_dist or summed_min_dists should be used
case 1:
// linear scoring
feats->set_value(fid_ef, 1.0/(min_dist+1));
break;
case 2:
// exponential scoring
feats->set_value(fid_ef, 1.0/exp(min_dist));
break;
case 3:
// relative scoring
feats->set_value(fid_ef, (j-i)/((j-i) + min_dist));
break;
default:
// binary scoring in case nothing is defined
feats->set_value(fid_ef, (summed_min_dists == 0));
}
return min_dist;
}
Array2D<WordID> src_tree; // src_tree(i,j) NT = type
unsigned int src_sent_len;
mutable Array2D<map<const TRule*, int> > fids_ef; // fires for fully lexicalized
int scoring_method;
};
ParseMatchFeatures::ParseMatchFeatures(const string& param) :
FeatureFunction(sizeof(WordID)) {
impl = new ParseMatchFeaturesImpl(param);
}
ParseMatchFeatures::~ParseMatchFeatures() {
delete impl;
impl = NULL;
}
void ParseMatchFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const {
int ants[8];
for (unsigned i = 0; i < ant_contexts.size(); ++i)
ants[i] = *static_cast<const int*>(ant_contexts[i]);
*static_cast<int*>(context) =
impl->FireFeatures(*edge.rule_, edge.i_, edge.j_, ants, features);
}
void ParseMatchFeatures::PrepareForInput(const SentenceMetadata& smeta) {
impl->InitializeGrids(smeta.GetSGMLValue("src_tree"), smeta.GetSourceLength());
}
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