NoReC baseline models

Baselines models trained on the NoReC dataset.

Dependencies

• sklearn
• gensim
• numpy
• scipy

Installation

To install dependencies, run:

$ pip install -r requirements.txt

To install package, run:

$ pip install .

Usage

Example using doc2vec with regression:

from norec_baselines import load_pipeline

pipeline = load_pipeline("path/to/models/", vectorizer="doc2vec", predictor="regression")

docs = # List of documents, each document being a list of tokens.

predictions = pipeline.predict(docs)

The regressor predicts a real-valued number in the approximate range of 1 to 6, while the classifier predicts an integer from 1 to 6.

Models

All the models were trained on the training set only. Input was tokenized and lowercased text.

Vectorizers / Document representation

• doc2vec – Doc2Vec from gensim with iter=55.
• bow – CountVectorizer from scikit-learn with the tokenizer and analyzer disabled.

Predictors

• classification – LogisticRegression from scikit-learn with C=0.1.
• regression – Ridge from scikit-learn with default settings.

Evaluation

Model	Accuracy	F1	R2
bow+classification	0.4986	0.3537	0.1754
bow+regression	0.4605	0.3186	0.2860
doc2vec+classification	0.4463	0.2737	0.0708
doc2vec+regression	0.4036	0.2165	0.1402

All models were evaluated on the dev set. When calculating accuracy on the regression models, predictions were rounded and capped to be in the range 1 to 6.

Comments to the evaluation results

Note that the parameter tuning of the classifiers and regression models are optimised with respect to accuracy and R², respectively. We evaluate all models with respect to both metrics however, in addition to macro-averaged F₁.

The results let us make at least three important observations: (1) The BoW representations give better results than doc2vec. This is likely because the relevant cues for predicting polarity are local properties in the documents. While doc2vec can be seen to average the contribution of all tokens in the document, the BoW representation allows the model to learn different weights for different words. This is especially important given that we work with relatively long documents (approx. 420 tokens on average). (2) We see that the models tend to perform best relative to the metric they where optimized for. However, we would argue that the regression approach, which has the strongest performance with respect to R², would be the most promising direction to pursue. While accuracy (and the classification-based models) treats all ratings as equally distinct, the R² metric takes into account the property that the ratings 2 and 3 are closer than 2 and 6. (3) There is still ample room for improvement, however. The performance metrics clearly show that these baselines results are exactly that; preliminary results that tell us something about the difficulty of the task, providing a point of reference to be improved upon.