Displaying 1 to 6 from 6 results

snips-nlu - Snips Python library to extract meaning from text

  •    Python

Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.

utterance_parser - Extract intent and entities from natural language utterances

  •    Ruby

A trainable natural language parser that extracts intent and entities from utterances. It uses a Naive Bayes classifier to determine intent and Conditional random fields to extract entities.

slot_filling_intent_joint_model - attention based joint model for intent detection and slot filling

  •    Python

Joint model for intent detection and slot filling based on attention, input alignment and knowledge. with ability to detect whether a input sentence is a noise input or meanfuling input by combine feature from domain detection, intent detection and slot filling.




fmr - Functional Meaning Representation and Semantic Parsing Framework

  •    Go

Semantic parsing is the process of mapping a natural language sentence into an intermediate logical form which is a formal representation of its meaning. Early semantic parsers used highly domain-specific meaning representation languages, with later systems using more extensible languages like Prolog, lambda calculus, lambda dependancy-based compositional semantics (λ-DCS), SQL, Python, Java, and the Alexa Meaning Representation Language. Some work has used more exotic meaning representations, like query graphs or vector representations.

ATIS.keras - Spoken Language Understanding(SLU)/Slot Filling in Keras

  •    Python

Spoken Language Understanding(SLU)/Slot Filling in Keras. Tutorial Implements RNNs in Keras to solve the Airline Travel Information System(ATIS) dataset.