learn to develop custom text classifiers with meaningcloud

Learn to Develop

Custom Text Classifiers

with MeaningCloud

October 5th, 2016

Webinar

Custom Text Classifiers with MeaningCloud

Before we get started…

Presenter

How to participate

• Send questions with the chat feature, or

• Click the “Raise your hand” button to speak

and we’ll enable your mic

• Afterwards, you’ll be able to access a recording of the

webinar and its contents as tutorials on our blog

Antonio Matarranz

CMO


The purpose of this webinar…

To learn how to implement

the highest-quality

text clasifiers

for your application


Agenda

Introduction to text classification

Text classification in MeaningCloud: API, built-in

models

Why to develop custom classifiers

Text classification technologies: machine learning,

rule-based, hybrid

Development of classification models in

MeaningCloud: tools, process

Practical case

Conclusions and Q&A


Text classification

Uses a predefined taxonomy = {categories}

Associate each text to one or several categories

Economy

Technology

Society

Sports

Taxonomy Class-

ification

Model


Classify: What? What for?

Text classification is the Text Analytics most extended task

Text

News

Web content

Contact center

Surveys

Social posts

Medical records

Application

Tag & Enrich

Targeted advertising

Analyze interaction

Interpret feedback

Understand conversation

Statistics


MeaningCloud: “Meaning as a Service”

Sign up, and use it for FREE at

http://www.meaningcloud.com

http://www.meaningcloud.com/


Text analytics, in the cloud (and on-premises)

Extract meaning and actionable insights from unstructured content

Automation of costly manual activities

MeaningCloud provides this service as a convenient, web-based offering

OpinionsFacts

Concepts

Organizations

People

Semantic

Analysis

Relationships

Themes


MeaningCloud’s APIs

Identifies occurrences of

names of people,

organizations, abstract

concepts, quantities, etc.

Theme classification

according to

predefined taxonomies

Identifies general and

attribute-level polarity

Distinguishes among 60

languages

Detailed morphosyntactic analysis Evaluates the impact of

opinions on several

reputational axes

Discover meaningful topics and

similarities among texts without

relying on predefined

taxonomies


MeaningCloud: standard classification models

‘Out-of-the-box’ support of

well-known predefined

classification standards

IPTC: news

IAB: targeted advertising

EuroVoc: public

administration

Social media: social

conversations

… and more to come

https://www.meaningcloud.com/developer/documentation/supported-models





Add-in for Excel

An experience fully integrated into Excel

Easy to use - No programming!

The most convenient way to evaluate, prototype, and use MeaningCloud

11


Let’s focus on a practical example

Yelp Reviews of restaurants in London


WE NEED AN EASY AND

POWERFUL WAY TO DEVELOP

CUSTOM CLASSIFIERS

Standard models are not enough


14

Example: custom classification for financial

services

The insights

that we need to

extract define

the classification

model


MeaningCloud customization tools


Classification technologies

Classifiers use patterns/vectors that represent each category

Technologies to generate those representations

• Statistic

• Rule-based

Training

documents

for category

Machine

learning

Rules for

category

Rule

codifier

Rule 1

Rule 2

Rule 3

Rule 4

Category

representation

Category

representation


Comparison between classification technologies

Statistical Rules

Benefits Fast, provided tagged texts

are available

Good accuracy for long texts

No false positives

Very good accuracy for non

extensive environments

Disadvantages “Black box” approach

False positives difficult to

correct

Bias in results, depending on

training

Costly if starting from scratch

False negatives, depending on

rule quality

Difficult to scale

Requires deep domain knowledge

Hybrid approach: best of both worlds


Defining a new category:

hybrid approach

Rule-based

Training-based

Possible to opt for one of

the approaches, or to

combine both, depending

on the application


Defining a category: training

Fed with precodified training texts

Based on machine learning technology

Training text


Defining a category: rules

Compulsory terms Forbidden terms

Terms that increase relevance Terms that decrease relevance


An iterative method to develop custom classifiers

Understand problem

Define taxonomy

Configure categories

Validate classification

Validate insights

Yes

End

Yes No

No

Examples

Rules

+ Examples

+ Rules

Modify

taxonomy


Our goal: world cuisine taxonomy


Rule example: American Food category


Where to get ideas for rules

Domain knowledge,

manual definition

Use n-gram extractor on

manually tagged texts

Extract information from manually

tagged texts using other tools, e.g.,

• Topic extraction

• Text clustering

Lists of terms featured in category-specific documents, e.g., Wikipedia

articles


Conclusions

The highest-quality text classification, at

your fingertips

Hybrid technology combines development

speed and accuracy

Customization tools allow to develop models

without programming

Agile development process, e.g.,

Configuration – Excel validation cycles

Machine

learning

Rules


Democratizing the extraction of meaning

High quality semantic analysis

Optimized technology mix

Continuously updates semantic resources

High-level APIs, e.g., User Profiling

Customizable to customer domain: models, dictionaries, sentiment

Affordable, risk-free

Mature, tested technology

Test and use for FREE (40,000 requests per month)

Pay per use

No commitment or permanence

Commercial plans for all needs

For developers and non

technical users

Add-in for Excel

Standard web services APIs

Plug-ins and SDKs for diverse environments and languages

Plug-and-play approach

OpinionesTemasHechos

Conceptos

Organizaciones

Personas

Relaciones


Q & A


Stay tuned to our emails and blog

We’ll be posting a recording of the webinar and

its contents (data and models) as tutorials soon!


Thank you for your attention!

Questions, suggestions...

Antonio Matarranz

CMO

[email protected]

http://www.meaningcloud.com

mailto:[email protected]

http://www.meaningcloud.com/