app.ddlog

chunk?(
    @key sent_id bigint,
    @key word_id bigint,
    tag text
).
tags(tag text).

# input data ##################################################################
words(
  sent_id bigint,
  word_id bigint,
  word text,
  pos text,
  true_tag text
).

word_embedding(
  word text,
  vec float[]
).

vec_dims(idx int).


word_vec(wid, vec):-
  words(s, wid, word, pos, true_tag),
  word_embedding(word_lower, vec),
  word_lower = lower(word).


# supervision / scope of tags #################################################
chunk(s, word, tag) = (
  if tag = true_tag then TRUE else FALSE end
) :-
  words(s, word, _, pos, true_tag),
  tags(tag).


# unigram features ###############################################################

@name("current word")
@weight(tag, word)
chunk(s, w, tag) :-
  words(s, w, word, pos, _).

@name("current pos tag")
@weight(tag, pos)
chunk(s, w, tag) :-
  words(s, w, word, pos, _).

@name("current and next pos")
@weight(tag, pos1, pos2)
chunk(s, w1, tag) :-
  w2 = w1 + 1,
  words(s, w1, word1, pos1, _),
  words(s, w2, word2, pos2, _).

@name("current and prev pos")
@weight(tag, pos1, pos2)
chunk(s, w2, tag) :-
  w2 = w1 + 1,
  words(s, w1, word1, pos1, _),
  words(s, w2, word2, pos2, _).


# bigram features ###############################################################

@name("linear chain")
@weight(tag1, tag2)
chunk(s, w1, tag1) ^ chunk(s, w2, tag2) :- w2 = w1 + 1.

@name("linear chain by pos")
@weight(tag1, tag2, pos1, pos2)
chunk(s, w1, tag1) ^ chunk(s, w2, tag2) :-
  w2 = w1 + 1,
  words(s, w1, word1, pos1, _),
  words(s, w2, word2, pos2, _).


# word embedding features ###############################################################

@name("word embedding")
@weight(tag, k)
@feature_value(vec[k])
chunk(s, w, tag) :-
  words(s, w, word, pos, _),
  word_vec(s, vec),
  vec_dims(k).