nordlys.logic.features.ftr_entity_similarity module

FTR Entity Similarity

Implements features capturing the similarity between entity and a query.

Author:Faegheh Hasibi

class nordlys.logic.features.ftr_entity_similarity.FtrEntitySimilarity(query, en_id, elastic)

Bases: object

DEBUG = 0

context_sim(mention, field='catchall')

LM score of entity to the context of query (context means query - mention)

E.g. given the query “uss yorktown charleston” and mention “uss”,

query context is ” yorktown charleston”

Parameters:

• mention – string
• field – field name

:return context similarity score

lm_score(field='catchall')

Query length normalized LM score between entity field and query

Parameters:field – field name

:return MLM score

mlm_score(field_weights)

Query length normalized MLM similarity between the entity and query

Parameters:field_weights – dictionary {field: weight, …}

:return MLM score

nllr(query, field_weights)

Computes Normalized query likelihood (NLLR):

NLLR(q,d) = sum_{t in q} P(t|q) log P(t| heta_d) - sum_{t in q} p(t|q) log P(t|C) where:

P(t|q) = n(t,q)/|q| P(t|C) = sum_{f} mu_f * P(t|C_f) P(t| heta_d) = smoothed LM/MLM score

Parameters:

• query – query
• field_weights – dictionary {field: weight, …}

Returns:

NLLR score