--- a
+++ b/wrapper_functions/scispacy_functions.py
@@ -0,0 +1,111 @@
+import spacy
+from scispacy.abbreviation import AbbreviationDetector
+from scispacy.hyponym_detector import HyponymDetector
+from scispacy.linking import EntityLinker
+from negspacy.negation import Negex
+
+def get_abbreviations(model, text):
+    """
+    returns a list of tuples in the form (abbreviation, expanded form), each element being a str
+    """
+
+    # logging
+    print(f"Identifying abbrevations using {model}")
+    partial_input = '\n'.join(text.split('\n')[:5])
+    print(f"Input text (truncated): {partial_input}\n...")
+
+    # abbreviation detection with scispacy
+    nlp = spacy.load(model)
+    nlp.add_pipe("abbreviation_detector")
+    doc = nlp(text)
+    abbreviations = [(abrv.text, abrv._.long_form.text) for abrv in doc._.abbreviations]
+
+    return abbreviations
+
+def get_hyponyms(model, text):
+    """
+    returns a list of tuples in the form (hearst_pattern, entity_1, entity_2, ...), each element being a str
+    """
+
+    # logging
+    print(f"Extracting hyponyms using {model}")
+    partial_input = '\n'.join(text.split('\n')[:5])
+    print(f"Input text (truncated): {partial_input}\n...")
+
+    # hyponym detection with scispacy
+    nlp = spacy.load(model)
+    nlp.add_pipe("hyponym_detector", last=True, config={"extended": True})
+    doc = nlp(text)
+    hearst_patterns = [tuple([str(element) for element in pattern]) for pattern in doc._.hearst_patterns]
+
+    return hearst_patterns
+
+def get_linked_entities(model, text):
+    """
+    returns a dictionary in the form {named entity: list of strings each describing one piece of linked information}
+    """
+
+    # logging
+    print(f"Entity linking using {model}")
+    partial_input = '\n'.join(text.split('\n')[:5])
+    print(f"Input text (truncated): {partial_input}\n...")
+
+    # entity linking with scispacy
+    output = {}
+
+    nlp = spacy.load(model)
+    nlp.add_pipe("scispacy_linker", config={"resolve_abbreviations": True, "linker_name": "umls"})
+    doc = nlp(text)
+
+    ents = doc.ents
+    linker = nlp.get_pipe("scispacy_linker")
+
+    for entity in ents:
+        cur = []
+        for umls_ent in entity._.kb_ents:
+            cur.append(str(linker.kb.cui_to_entity[umls_ent[0]]))
+        output[entity] = cur
+
+    return output
+
+def get_named_entities(model, text):
+    """
+    returns a list of strings, each string is an identified named entity
+    """
+
+    # logging
+    print(f"Extracting named entities using {model}")
+    partial_input = '\n'.join(text.split('\n')[:5])
+    print(f"Input text (truncated): {partial_input}\n...")
+
+    # named recognition with scispacy
+    nlp = spacy.load(model)
+    doc = nlp(text)
+    named_entities = [str(ent) for ent in doc.ents]
+
+    return named_entities
+
+
+def get_negation_entities(model, text):
+    """
+    returns a list of pairs, default model is "en_core_web_sm"
+    Negspacy is a spaCy pipeline component that evaluates whether Named Entities are negated in text.
+    Example:
+    >> test = get_negation_entities("en_core_web_sm","She does not like Steve Jobs but likes Apple products.")
+    >> print (test)
+    [(True, 'Steve Jobs'), (False, 'Apple')]
+    """
+
+    # logging
+    print(f"Extracting whether Named Entities are negated using {model}")
+    partial_input = '\n'.join(text.split('\n')[:5])
+    print(f"Input text (truncated): {partial_input}\n...")
+
+    # named recognition with scispacy
+    nlp = spacy.load(model)
+    nlp.add_pipe("negex", config={"ent_types":["PERSON","ORG","NORP","GPE"]})
+    doc = nlp(text)
+    pairs = [(ent._.negex,ent.text) for ent in doc.ents]
+
+    return pairs
+