clef-2019's Introduction

CLEF 2019

Core approach:
- We have used a hybrid approach in which we use MART algorithm with 421 linguistic based features. Next, we apply a rule-based approach to decrease the ranks of sentences with phrases like “thank you”. We set the number of trees to 50 and the number of leaves to 2.
Important/interesting/novel novel representations used (features, embeddings...):
- Primary: Our features include:
  - the existence of named entities identified by Stanford NLP tool [1].
  - the topic of sentences identified by IBM Watson’s NLP tool [2].
  - POS tags, bi-grams that appear at least 50 times in only check-worthy sentences or only not-check-worthy sentences in the training set.
  - whether the sentence is a question sentence or not.
- Contrastive 1: In addition to features used in our primary method, we also used the speaker of the statements as features.
- Contrastive 2: We used the same features as Contrastive 1 but we used logistic regression instead of MART.
Important/interesting/novel tools used:
- We used IBM-Watson, Stanford-NLP tool for feature extraction and Ranklib and sci-kit libraries for MART and logistic regression models, respectively.
Significant data pre/post-processing:
- We have not applied any preprocessing step.
Other data used (outside of the provided):
- We have not used any external data. We have just used the provided training data.
References (if applicable):

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