# LemmaExpander **`class LemmaExpander(config)`** Bases: [`BatchedStep`](../technical/libnlp/steps/standard.md#squirro.lib.nlp.steps.batched_step.BatchedStep) Keep track of lemmatised version of relevant terms. **Note** - Can leverage pos_mutations from POSBooster to tag lemmas only for important terms. - This metadata is then used to generate exact vs lemmatised ES query clauses (by ES query generator) **Input** - Uses field `nlp` (spacy.tokens.doc.Doc) that contains the analysed SpaCy Doc (from SpacyNormalizer). **Output** - Writes field `lemma_map` (dict) that contains the mapping of original term and its root form like `{'original': 'lemmatized'}` Parameters - `step` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`, ``"app"`) – app - `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`, ``"query_processing"`) – query_processing - `name` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`, ``"lemma_tagger"`) – lemma_tagger - `analyzed_input_field` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`,``"nlp"`) – query - `output_field` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`,``"lemma_map"`) – Tagged lemma terms - `path` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`,``"."`) – . Methods SummaryMethods Documentation **`process_doc(doc)`** Process a document Parameters `doc` ([`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)) – Document Returns Processed document Return type [Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)