In this tutorial, we will apply CrowdTruth metrics to a sparse multiple choice crowdsourcing task for Relation Extraction from sentences. The workers were asked to read a sentence with 2 highlighted terms, then pick from a multiple choice list what are the relations expressed between the 2 terms in the sentence. The options available in the multiple choice list change with the input sentence. The task was executed on FigureEight. For more crowdsourcing annotation task examples, click here.
To replicate this experiment, the code used to design and implement this crowdsourcing annotation template is available here: template, css, javascript.
This is a screenshot of the task as it appeared to workers:
A sample dataset for this task is available in this file, containing raw output from the crowd on FigureEight. Download the file and place it in a folder named data that has the same root as this notebook. Now you can check your data:
In [1]:
import pandas as pd
test_data = pd.read_csv("../data/relex-sparse-multiple-choice.csv")
test_data.head()
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In [2]:
import crowdtruth
from crowdtruth.configuration import DefaultConfig
Our test class inherits the default configuration DefaultConfig, while also declaring some additional attributes that are specific to the Relation Extraction task:
inputColumns: list of input columns from the .csv file with the input dataoutputColumns: list of output columns from the .csv file with the answers from the workersannotation_separator: string that separates between the crowd annotations in outputColumnsopen_ended_task: boolean variable defining whether the task is open-ended (i.e. the possible crowd annotations are not known beforehand, like in the case of free text input); in the task that we are processing, workers pick the answers from a pre-defined list, therefore the task is not open ended, and this variable is set to Falseannotation_vector: list of possible crowd answers, mandatory to declare when open_ended_task is False; for our task, this is the list of all relations that were given as input to the crowd in at least one sentenceprocessJudgments: method that defines processing of the raw crowd data; for this task, we process the crowd answers to correspond to the values in annotation_vectorThe complete configuration class is declared below:
In [3]:
class TestConfig(DefaultConfig):
inputColumns = ["sent_id", "term1", "b1", "e1", "term2", "b2", "e2", "sentence", "input_relations"]
outputColumns = ["output_relations"]
annotation_separator = "\n"
# processing of a closed task
open_ended_task = False
annotation_vector = [
"title", "founded_org", "place_of_birth", "children", "cause_of_death",
"top_member_employee_of_org", "employee_or_member_of", "spouse",
"alternate_names", "subsidiaries", "place_of_death", "schools_attended",
"place_of_headquarters", "charges", "origin", "places_of_residence",
"none"]
def processJudgments(self, judgments):
# pre-process output to match the values in annotation_vector
for col in self.outputColumns:
# transform to lowercase
judgments[col] = judgments[col].apply(lambda x: str(x).lower())
return judgments
In [4]:
data, config = crowdtruth.load(
file = "../data/relex-sparse-multiple-choice.csv",
config = TestConfig()
)
data['judgments'].head()
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In [5]:
results = crowdtruth.run(data, config)
results is a dict object that contains the quality metrics for sentences, relations and crowd workers.
The sentence metrics are stored in results["units"]:
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results["units"].head()
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The uqs column in results["units"] contains the sentence quality scores, capturing the overall workers agreement over each sentence. Here we plot its histogram:
In [7]:
import matplotlib.pyplot as plt
%matplotlib inline
plt.hist(results["units"]["uqs"])
plt.xlabel("Sentence Quality Score")
plt.ylabel("Sentences")
Out[7]:
The unit_annotation_score column in results["units"] contains the sentence-relation scores, capturing the likelihood that a relation is expressed in a sentence. For each sentence, we store a dictionary mapping each relation to its sentence-relation score.
In [8]:
results["units"]["unit_annotation_score"].head(10)
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The worker metrics are stored in results["workers"]:
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results["workers"].head()
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The wqs columns in results["workers"] contains the worker quality scores, capturing the overall agreement between one worker and all the other workers.
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plt.hist(results["workers"]["wqs"])
plt.xlabel("Worker Quality Score")
plt.ylabel("Workers")
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The relation metrics are stored in results["annotations"]. The aqs column contains the relation quality scores, capturing the overall worker agreement over one relation.
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results["annotations"]
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