(shubham srivastava) #1

I am facing issue while extracting entity which has same pattern

both sentence are similar except its beginning, how can I differentiate both and make entity extraction accurate.

training data



@akelad @JulianGerhard @Juste @juste_petr

(Shanu) #2

Am also facing similiar issue can anyone help??? @akelad @JulianGerhard @Juste @shubham

(JG) #3

hey @shubham, I can suggest you to try out Regex Feature for these problem, you can add the patterns for the above sentences:

(shubham srivastava) #4

it’s still not working for me, entity extraction is not accurate. can anyone help me with this ?

where the should do the changes in my training data or should I do something EntityExtractor?

@akelad @JulianGerhard @Juste

(Akela Drissner) #5

what does your nlu config look like?

(shubham srivastava) #6

language: en pipeline:

  • name: “SpacyNLP” model: “en_core_web_lg”

  • name: “SpacyTokenizer”

  • name: “SpacyFeaturizer”

  • name: “RegexFeaturizer”

  • name: “CRFEntityExtractor”

    features: [ [“low”, “title”, “upper”], [“bias”, “low”, “prefix5”, “prefix2”, “suffix5”,“digit”, “suffix3”,“suffix2”,“upper”, “title” ,“pattern”], [“low”, “title”, “upper”] ] BILOU_flag: true max_iterations: 50 L1_c: 0.1 L2_c: 0.1

  • name: “EntitySynonymMapper”

  • name: “CountVectorsFeaturizer” stop_words: [‘ourselves’, ‘hers’, ‘between’, ‘yourself’, ‘but’, ‘again’, ‘there’, ‘about’, ‘once’, ‘during’, ‘out’, ‘very’, ‘having’, ‘with’, ‘they’, ‘own’, ‘an’, ‘be’, ‘some’, ‘for’, ‘do’, ‘its’, ‘yours’, ‘such’, ‘into’, ‘of’, ‘most’, ‘itself’, ‘off’, ‘is’, ‘s’, ‘am’, ‘or’, ‘who’, ‘as’, ‘from’, ‘him’, ‘each’, ‘the’, ‘themselves’, ‘until’, ‘below’, ‘are’, ‘we’, ‘these’, ‘your’, ‘his’, ‘through’, ‘don’, ‘nor’, ‘me’, ‘were’, ‘her’, ‘more’, ‘himself’, ‘this’, ‘down’, ‘should’, ‘our’, ‘their’, ‘while’, ‘above’, ‘both’, ‘up’, ‘to’, ‘ours’, ‘had’, ‘she’, ‘all’, ‘no’, ‘when’, ‘at’, ‘any’, ‘before’, ‘them’, ‘same’, ‘and’, ‘been’, ‘have’, ‘in’, ‘will’, ‘on’, ‘does’, ‘yourselves’, ‘then’, ‘that’, ‘because’, ‘what’, ‘over’, ‘why’, ‘so’, ‘can’, ‘did’, ‘not’, ‘now’, ‘under’, ‘he’, ‘you’, ‘herself’, ‘has’, ‘just’, ‘where’, ‘too’, ‘only’, ‘myself’, ‘which’, ‘those’, ‘i’, ‘after’, ‘few’, ‘whom’, ‘t’, ‘being’, ‘if’, ‘theirs’, ‘my’, ‘against’, ‘a’, ‘by’, ‘doing’, ‘it’, ‘how’, ‘further’, ‘was’, ‘here’, ‘than’]

  • name: “EmbeddingIntentClassifier” intent_tokenization_flag: true intent_split_symbol: “+”


  • name: MemoizationPolicy
  • name: KerasPolicy
  • name: MappingPolicy