Unlock meaning like never before

A robust set of tools for intelligent text understanding

Codeq's proprietary NLP technology offers advanced Deep Learning models and linguistic analysis tools to extract rich representations from your textual data. Our API provides the backend to take your application to the next level.

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Features

  • Powerful hybrid modules: Machine Learning/Deep Learning + Rule Based
  • Boutique-like domain customization
  • Simple API design and integration
  • Flexible deployment options
  • Developed in Python

High Level Modules

  • Named Entity Recognition
  • Named Entity Linking
  • Named Entity Salience
  • Speech Act Classifier *
  • Question Classifier *
  • Sentiment Classifier
  • Emotion Classifier *
  • Sarcasm Classifier *
  • Coreference Resolution
  • Date Resolution
  • Task Extraction
  • Sentence Compression *
  • Summarization

Low Level Modules

  • Language Identifier
  • Tokenization
  • Sentence Splitting
  • Stopword Removal
  • Stemming
  • True Casing
  • Detrue Casing
  • POS Tagging
  • Lemmatization
  • Dependency Parser
  • Chunker
  • Semantic Role Labeling *

* NLP tools you will not find in other APIs.

Why Codeq NLP API?

Understand your users

Extract rich insight into what users are expressing in your application by identifying a wide range of language utterances.

I'm writing because I'm worried about my retirement plan. I'm about to retire in a year but this nasty market crash has slashed my life investments in half. I really need your advice!

I'm writing because I'm worried about my retirement plan.
sentiments: Negative
emotions: Angst
sarcasm: Non-sarcastic
speech_acts: Statement

I'm about to retire in a year but this nasty market crash has slashed my life investments in half.
sentiments: Negative
emotions: Disgust/Dislike
sarcasm: Non-sarcastic
speech_acts: Statement

I really need your advice!
sentiments: Neutral
emotions: No emotion
sarcasm: Non-sarcastic
speech_acts: Desire/Need

Get a 360 degree overview of your information

Spot the people, places, organizations, and dates discussed in your texts. We enrich your content with semantic knowledge, so you can dissect the subject easily.

Tesla ORG almost died earlier this year, Elon Musk PER said in an interview with AXIOS ORG that aired on HBO ORG . Musk PER said the company was "bleeding money like crazy" as it worked through the Model 3 MISC production ramp in the spring and summer.

Teslascore: 0.9654
American automotive, energy storage and solar power company
https://en.wikipedia.org/wiki/Tesla,_Inc.

Elon Muskscore: 0.9959
South African-born American entrepreneur
https://en.wikipedia.org/wiki/Elon_Musk

AXIOS Mediascore: 0.0052
American news and information website
https://en.wikipedia.org/wiki/Axios_(website)

HBOscore: 0.1508
American pay television network
https://en.wikipedia.org/wiki/HBO

Tesla Model 3score: 0.0087
all-electric four-door compact luxury sedan
https://en.wikipedia.org/wiki/Tesla_Model_3

Get a 360 degree overview of your information

Spot the people, places, organizations, and dates discussed in your texts. We enrich your content with semantic knowledge, so you can dissect the subject easily.

Extract actionable content that needs your attention

Our API can analyze your unstructured content and extract important tasks, including a concise representation of priority patterns.

Hi John, we need to finish the templates this week. Please send me your changes as soon as you can. Also let me know your available times for the meeting before the end of the next week.

Hi John, we need to finish the templates this week.
speech_acts: Desire/Need
is_task: False

Please send me your changes as soon as you can.
speech_acts: Desire/Need
is_task: True
task_priority: urgent

Also let me know your available times for the meeting before the end of the next week.
speech_acts: Desire/Need
is_task: True
task_priority: medium - 6 remaining days

Start in minutes

Create a user account to generate your user credentials, install our Python SDK and you are ready to start analyzing your texts.


# pip install codeq-nlp-api

from codeq_nlp_api import CodeqClient

client = CodeqClient(user_id="YOUR_USER_ID", user_key="YOUR_USER_KEY")

text = "This model is an expensive alternative with useless battery."
document = client.analyze(text)
                

Start in minutes

Create an account to generate your user credentials, then install our Python SDK. You're ready to start analyzing your texts!

Powerful customization

Define your own NLP pipeline based on the linguistic tools that you require for your application.


pipeline = "speech_acts, emotions, sentiment, sarcasm"

text = "This model is an expensive alternative with useless battery."
document = client.analyze(text, pipeline)

for sentence in document.sentences:
    print(sentence)

"""
Output:
{
  "speech_acts": "['Statement']",
  "sentiments": "['Negative']",
  "emotions": "['Disgust/Dislike']",
  "sarcasm": "Non-sarcastic",
}
"""
                

Learn more in our documentation section and sign up to obtain a user ID and key.

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