added non-pruning intersection and a CRF tagger

- the linear-chain tagger is more of a proof of concept than a real tagger-- the context-free assumptions made in a number of places mean that the algorithms used may not be as efficient as they could be, but the model is as powerful as any CRF
- it would be easy to add latent variables or semi-CRF support (or both!)
- i've added a couple basic features that are often used for POS tagging
- non-pruning intersection is useful for lexical word alignment models and the tagger
- a sample POS tagger model will be committed later

1 files changed, 3 insertions, 3 deletions

diff --git a/decoder/apply_models.h b/decoder/apply_models.h
index 08fce037..d6d8b34a 100644
--- a/decoder/apply_models.h
+++ b/decoder/apply_models.h
@@ -5,16 +5,16 @@ struct ModelSet;
 struct Hypergraph;
 struct SentenceMetadata;
 
-struct PruningConfiguration {
+struct IntersectionConfiguration {
   const int algorithm; // 0 = full intersection, 1 = cube pruning
   const int pop_limit; // max number of pops off the heap at each node
-  explicit PruningConfiguration(int k) : algorithm(1), pop_limit(k) {}
+  IntersectionConfiguration(int alg, int k) : algorithm(alg), pop_limit(k) {}
 };
 
 void ApplyModelSet(const Hypergraph& in,
                    const SentenceMetadata& smeta,
                    const ModelSet& models,
-                   const PruningConfiguration& config,
+                   const IntersectionConfiguration& config,
                    Hypergraph* out);
 
 #endif