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+++ b/medacy/pipelines/drug_event_pipeline.py
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+import spacy
+
+from medacy.pipeline_components.feature_extractors.discrete_feature_extractor import FeatureExtractor
+from medacy.pipeline_components.feature_overlayers.lexicon_component import LexiconOverlayer
+from medacy.pipeline_components.feature_overlayers.metamap.metamap_all_types_component import MetaMapAllTypesOverlayer
+from medacy.pipeline_components.feature_overlayers.table_matcher_component import TableMatcherOverlayer
+from medacy.pipeline_components.learners.crf_learner import get_crf
+from medacy.pipeline_components.tokenizers.character_tokenizer import CharacterTokenizer
+from medacy.pipelines.base.base_pipeline import BasePipeline
+
+
+class DrugEventPipeline(BasePipeline):
+    """
+    Pipeline for recognition of adverse drug events from the 2018/19 FDA OSE drug label challenge
+
+    Created by Corey Sutphin of NLP@VCU
+    """
+
+    def __init__(self, entities, metamap=None, lexicon={}, **kwargs):
+        """
+        Init a pipeline for processing data related to identifying adverse drug events
+        :param entities: a list of entities
+        :param metamap: instance of MetaMap
+        :param entities: entities to be identified, for this pipeline adverse drug events
+        :param lexicon: Dictionary with labels and their corresponding lexicons to match on
+        """
+        super().__init__(entities, spacy_pipeline=spacy.load("en_core_web_sm"), **kwargs)
+
+        if metamap:
+            self.add_component(MetaMapAllTypesOverlayer, metamap)
+
+        if lexicon is not None:
+            self.add_component(LexiconOverlayer, lexicon)
+
+        self.add_component(TableMatcherOverlayer)
+
+    def get_learner(self):
+        return "CRF_l2sgd", get_crf()
+
+    def get_tokenizer(self):
+        return CharacterTokenizer(self.spacy_pipeline)
+
+    def get_feature_extractor(self):
+        return FeatureExtractor(window_size=3, spacy_features=['pos_', 'shape_', 'prefix_', 'suffix_', 'like_num', 'text', 'head'])