Dataset is read by CoNLL_Reader. You can directly reference a dataset entry by subscribing with entry index. To use an encoding scheme you need to initialize one underparadigms.py. The most recent scheme that is of researcher's interest is paradigms.DIRECTTAG. A word's role to a predicate is indicated by labeling the role explicitly and stating as a prefix of (>){1,mul}|(<){1,mul} This takes two parameters in initialization. First one mul indicating the furthest distance a verb can be away from a given label in terms of verb distance (how many other verbs are in between). Second one is ommitlemma depending on which verb's lemma is not included but only its sense (e.g V01 instead of Vmake.01).
from semantictagger.paradigms import DIRECTTAG
from semantictagger.dataset import Dataset
from pathlib import Path
fp = Path("./UP_English-EWT/en_ewt-up-train.conllu")
dataset = Dataset(fp)
# Get 10th sentence
entry = dataset[10]
# Get all SRL annotations
# Depth indicates how many verbs there are.
annotations = [entry.get_srl_annotation(d) for d in range(entry.depth)]
#Init tagger
dirtag = DIRECTTAG(2, omitlemma=True)
# Encode and decode
encoded = dirtag.encode(entry)
decoded = dirtag.decode(encoded)
#Or simply call
# Which returns how many labeled could be retrieved once
# the tagger is applied to the given entry
countcorrecttags , countfalsetags = dirtag.test(entry)
# Returns
# -------
# Sparsity :How many empty tags there are compared to nonempty ones.
# Mean of the set (emptytag excluded)
# Std (emptytag excluded)
# Frequency dictionary
# Results are printed if show_results = True
sparsity , mean , std , dict_ = dataset.getlabelfrequencies(dirtag ,show_results = True , returndict = True)
from semantictagger.datastats import collectaccuracy
"""
Collects info from test results over entire dataset.
Return
------
correct : How many sentences correctly labeled
false : How many sentences incorrectly labeled
singlecorrect : How many tokens correctly labeled
singlefalse : How many tokens incorrectly labeled
"""
(correct , false), (singlecorrect ,singlefalse) = collectaccuracy(dirtag , dataset , showresults= True)- Dataset entry no 269 has two verbs in one annotation level. How to solve this problem?
- Sentence no. 5081 has two exactly same semantic role layers (verbs coincide). What to do with it ?