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classifier nlp

watson natural language classifier | ibm

watson natural language classifier | ibm

Get started with natural language processing and machine learning in 15 minutes or less. Easily categorize text with custom labels to automate workflows, extract insights, and improve search and discovery.

Generate higher accuracy on less training data through NLCs ensemble of machine learning techniques. NLC models include multiple Support Vector Machines (SVMs) and a Convolutional Neural Network (CNNs), using IBMs Deep Learning-as-a-Service (DLaaS).

Classify text in multiple languages, including English, Arabic, French, German, Italian, Japanese, Korean, Portuguese (Brazilian), and Spanish.

Build, train, and manage classifiers, regardless of technical skills. Access NLC capabilities through the API or an easy-to-use interface in Watson Studio.

At the core of natural language processing (NLP) lies text classification. Watson Natural Language Classifier (NLC) allows users to classify text into custom categories, at scale. Developers without a background in machine learning (ML) or NLP can enhance their applications using this service. NLC combines various advanced ML techniques to provide the highest accuracy possible, without requiring a lot of training data.

Behind the scenes, NLC utilizes an ensemble of classification models, along with unsupervised and supervised learning techniques, to achieve its accuracy levels. After your training data is assembled, NLC evaluates your data against multiple support vector machines (SVMs) and a convolutional neural network (CNN) using IBMs Deep Learning As a Service (DLaaS).

Text classification use cases and case studies Text classification is foundational for most natural language processing and machine learning use cases. Today, companies use text classification to flag inappropriate comments on social media, understand sentiment in customer reviews, determine whether email is sent to the inbox or filtered into the spam folder, and more.

Here are some examples of how companies across industries are using topic categorization from Watson Natural Language Classifier to improve workflows and transform the customer experience:

step 7: train the natural language processing classifiers the conversational ai playbook 4.3.4rc5 documentation

step 7: train the natural language processing classifiers the conversational ai playbook 4.3.4rc5 documentation

The Natural Language Processor (NLP) in MindMeld is tasked with understanding the user's natural language input. It analyzes the input using a hierarchy of classification models. Each model assists the next tier of models by narrowing the problem scope, or in other words successively narrowing down the 'solution space.'

To train the NLP classifiers for our Kwik-E-Mart store information app, we must first gather the necessary training data as described in Step 6. Once the data is ready, we open a Python shell and start building the components of our natural language processor.

This method trains all models in the specified NLP pipeline. The Natural Language Processor automatically infers which classifiers need to be trained based on the directory structure and the annotations in the training data. In our case, the NLP will train an intent classifier for the store_info domain and entity recognizers for each intent that contains labeled queries with entity annotations. Domain classification and role classification models will not be built because our simple example did not include training data for them.

By default, the build() method shown above uses the baseline machine learning settings for all classifiers, which should train reasonable models in most cases. To further improve model performance, MindMeld provides extensive capabilities for optimizing individual model parameters and measuring results. We'll next explore how to experiment with different settings for each NLP component individually.

The domain classifier (also called the domain model) is a text classification model that is trained using the labeled queries across all domains. Our simple app only has one domain and hence does not need a domain classifier. However, complex conversational apps such as the popular virtual assistants on smartphones and smart speakers today have to handle queries from varied domains such as weather, navigation, sports, finance, and music, among others. Such apps use domain classification as the first step to narrow down the focus of the subsequent classifiers in the NLP pipeline.

The NaturalLanguageProcessor class in MindMeld exposes methods for training, testing, and saving all the models in our classifier hierarchy, including the domain model. For example, suppose we want to build a logistic regression classifier that does domain classification. In our Python shell, we start off by instantiating an object of the NaturalLanguageProcessor class. We then train the domain_classifier model by calling its fit() method.

In addition to the model parameter we used above, the fit() method also takes parameters we can use to improve upon the baseline SVM model trained by default. These include parameters for features, cross-validation settings, and other model-specific configuration. See the User Guide for details.

Intent classifiers (also called intent models) are text classification models that are trained, one-per-domain, using the labeled queries in each intent folder. Our Kwik-E-Mart app supports multiple intents (e.g. greet, get_store_hours, find_nearest_store, etc.) within the store_info domain. We will now see how to train an intent classifier that correctly maps user queries to one of these supported intents.

Training our intent model is similar to training the domain model using the NaturalLanguageProcessor class, but this time we explicitly define the features and cross-validation settings we want to use. For our intent classifier, let us assume that we want to build a logistic regression model and use bag of words and edge n-grams as features. Also, we would like to do k-fold cross validation with 10 splits to find the ideal hyperparameter values.

Finally, we fetch the intent_classifier for the domain we are interested in and call its fit() method to train the model. The code below shows how to train an intent classifier for the store_info domain in our Kwik-E-Mart app.

We have now successfully trained an intent classifier for the store_info domain. If our app had more domains, we would follow the same procedure for those other domains. We can test the trained intent model on a new queryby calling its predict() and predict_proba() methods.

See the User Guide for a comprehensive list of the different model, feature extraction and hyperparameter settings for training the domain and intent models. The User Guide also describes how to evaluate trained models using labeled test data.

Entity recognizers (also called entity models) are sequence labeling models that are trained per intent using all the annotated queries in a particular intent folder in the domains directory. The entity recognizer detects the entities within a query, and labels them as one of the pre-defined entity types.

From the model hierarchy we defined for our Kwik-E-Mart app in Step 3, we can see that the get_store_hours intent depends on two types of entities. Of these, sys_time is a system entity that MindMeld recognizes automatically. The store_name entity, on the other hand, requires custom training data and a trained entity model. Let's look at how to use the NaturalLanguageProcessor class to train entity recognizers for detecting custom entities in user queries.

In this example we use a Maximum Entropy Markov Model, which is a good choice for sequence labeling tasks like entity recognition. The features we use include a gazetteer , which is a comprehensive list of popular entity names. Gazetteers are among the most powerful and commonly used sources of information in entity recognition models. Our example gazetteer for the store_name entity type is a list of all the Kwik-E-Mart store names in our catalog, stored in a text file called gazetteer.txt and located in the appropriate subdirectory of the entities folder. MindMeld automatically utilizes any gazetteer named gazetteer.txt that is located within an entity folder. The example gazetteer file looks like this:

When words in a query fully or partly match a gazetteer entry, that can be used to derive features. This makes gazetteers particularly helpful for detecting entities which might otherwise seem to be a sequence of common nouns, such as main street, main and market, and so on. Apart from using gazetteer-based features, we'll use the bag of n-grams surrounding the token as additional features. Finally, we'll continue using 10-fold cross validation as before.

We have now trained and saved the entity recognizer for the get_store_hours intent. If more entity recognizers were required, we would have repeated the same procedure for each entity in each intent. We test the trained entity recognizer using its predict() method.

Role classifiers (also called role models) are trained per entity using all the annotated queries in a particular intent folder. Roles offer a way to assign an additional distinguishing label to entities of the same type. Our simple Kwik-E-Mart application does not need a role classification layer. However, consider a possible extension to our app, where users can search for stores that open and close at specific times. As we saw in the example in Step 6, this would require us to differentiate between the two sys_time entities by recognizing one as an open_time and the other as a close_time. This can be accomplished by training an entity-specific role classifier that assigns the correct role label for each such sys_time entity detected by the Entity Recognizer.

Let's walk through the process of using MindMeld to train a role classifier for the sys_time entity type. The workflow is just like the previous classifiers: instantiate a NaturalLanguageProcessor object; access the classifier of interest (in this case, the role_classifier for the sys_time entity); define the machine learning settings; and, call the fit() method of the classifier. For this example, we will just use MindMeld's default configuration (Logistic Regression) to train a baseline role classifier without specifying any additional training settings. For the sake of code readability, we retrieve the classifier of interest in two steps: first get the object representing the current intent, then fetch the role_classifier object of the appropriate entity under that intent.

The Kwik-E-Mart blueprint distributed with MindMeld does not use role classification. The code snippet below shows a possible extension to the app where the sys_time entity is further classified into two different roles.

Once the classifier is trained, we test it on a new query using the familiar predict() method. The predict() method of the role classifier requires both the full input query and the set of entities predicted by the entity recognizer.

Here is a different example of role classification from the Home Assistant blueprint. The home assistant app leverages roles to correctly implement the functionality of changing alarms, e.g. "Change my 6 AM alarm to 7 AM".

The entity resolver component of MindMeld maps each identified entity to a canonical value. For example, if your application is used for browsing TV shows, you may want to map both entity strings funny and hilarious to a pre-defined genre code like Comedy. Similarly, in a music app, you may want to resolve both Elvis and The King to the artist Elvis Presley (ID=20192), while making sure not to get confused by Elvis Costello (ID=139028). Entity resolution can be straightforward for some classes of entities. For others, it can be complex enough to constitute the dominant factor limiting the overall accuracy of your application.

MindMeld provides advanced capabilities for building a state-of-the-art entity resolver. As discussed in Step 6, each entity type can be associated with an optional entity mapping file. This file specifies, for each canonical concept, the alternate names or synonyms with which a user may refer to this concept. In the absence of an entity mapping file, the entity resolver cannot resolve the entity. Instead, it logs a warning and skips adding a value attribute to the entity. For example, the following code illustrates the output of the natural language processor when an entity mapping data file is absent for the store_name entity:

If an entity mapping file is specified, as illustrated in Step 6, the entity resolver resolves the entity to a defined ID and canonical name. It assigns these to the value attribute of the entity, in the form of an object. Then the output of the natural language processor could resemble the following.

guide to text classification with machine learning & nlp

guide to text classification with machine learning & nlp

Text classification is a machine learning technique that assigns a set of predefined categories to open-ended text. Text classifiers can be used to organize, structure, and categorize pretty much any kind of text from documents, medical studies and files, and all over the web.

For example, new articles can be organized by topics; support tickets can be organized by urgency; chat conversations can be organized by language; brand mentions can be organized by sentiment; and so on.

Its estimated that around 80% of all information is unstructured, with text being one of the most common types of unstructured data. Because of the messy nature of text, analyzing, understanding, organizing, and sorting through text data is hard and time-consuming, so most companies fail to use it to its full potential.

This is where text classification with machine learning comes in. Using text classifiers, companies can automatically structure all manner of relevant text, from emails, legal documents, social media, chatbots, surveys, and more in a fast and cost-effective way. This allows companies to save time analyzing text data, automate business processes, and make data-driven business decisions.

Manually analyzing and organizing is slow and much less accurate.. Machine learning can automatically analyze millions of surveys, comments, emails, etc., at a fraction of the cost, often in just a few minutes. Text classification tools are scalable to any business needs, large or small.

There are critical situations that companies need to identify as soon as possible and take immediate action (e.g., PR crises on social media). Machine learning text classification can follow your brand mentions constantly and in real time, so you'll identify critical information and be able to take action right away.

Human annotators make mistakes when classifying text data due to distractions, fatigue, and boredom, and human subjectivity creates inconsistent criteria. Machine learning, on the other hand, applies the same lens and criteria to all data and results. Once a text classification model is properly trained it performs with unsurpassed accuracy.

Automatic text classification applies machine learning, natural language processing (NLP), and other AI-guided techniques to automatically classify text in a faster, more cost-effective, and more accurate manner.

Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category.

Say that you want to classify news articles into two groups: Sports and Politics. First, youll need to define two lists of words that characterize each group (e.g., words related to sports such as football, basketball, LeBron James, etc., and words related to politics, such as Donald Trump, Hillary Clinton, Putin, etc.).

Next, when you want to classify a new incoming text, youll need to count the number of sport-related words that appear in the text and do the same for politics-related words. If the number of sports-related word appearances is greater than the politics-related word count, then the text is classified as Sports and vice versa.

For example, this rule-based system will classify the headline When is LeBron James' first game with the Lakers? as Sports because it counted one sports-related term (LeBron James) and it didnt count any politics-related terms.

Rule-based systems are human comprehensible and can be improved over time. But this approach has some disadvantages. For starters, these systems require deep knowledge of the domain. They are also time-consuming, since generating rules for a complex system can be quite challenging and usually requires a lot of analysis and testing. Rule-based systems are also difficult to maintain and dont scale well given that adding new rules can affect the results of the pre-existing rules.

Instead of relying on manually crafted rules, machine learning text classification learns to make classifications based on past observations. By using pre-labeled examples as training data, machine learning algorithms can learn the different associations between pieces of text, and that a particular output (i.e., tags) is expected for a particular input (i.e., text). A tag is the pre-determined classification or category that any given text could fall into.

The first step towards training a machine learning NLP classifier is feature extraction: a method is used to transform each text into a numerical representation in the form of a vector. One of the most frequently used approaches is bag of words, where a vector represents the frequency of a word in a predefined dictionary of words.

For example, if we have defined our dictionary to have the following words {This, is, the, not, awesome, bad, basketball}, and we wanted to vectorize the text This is awesome, we would have the following vector representation of that text: (1, 1, 0, 0, 1, 0, 0).

Then, the machine learning algorithm is fed with training data that consists of pairs of feature sets (vectors for each text example) and tags (e.g. sports, politics) to produce a classification model:

Once its trained with enough training samples, the machine learning model can begin to make accurate predictions. The same feature extractor is used to transform unseen text to feature sets, which can be fed into the classification model to get predictions on tags (e.g., sports, politics):

Text classification with machine learning is usually much more accurate than human-crafted rule systems, especially on complex NLP classification tasks. Also, classifiers with machine learning are easier to maintain and you can always tag new examples to learn new tasks.

One of the members of that family is Multinomial Naive Bayes (MNB) with a huge advantage, that you can get really good results even when your dataset isnt very large (~ a couple of thousand tagged samples) and computational resources are scarce.

Naive Bayes is based on Bayess Theorem, which helps us compute the conditional probabilities of the occurrence of two events, based on the probabilities of the occurrence of each individual event. So were calculating the probability of each tag for a given text, and then outputting the tag with the highest probability.

This means that any vector that represents a text will have to contain information about the probabilities of the appearance of certain words within the texts of a given category, so that the algorithm can compute the likelihood of that text belonging to the category.

Support Vector Machines (SVM) is another powerful text classification machine learning algorithm, becauseike Naive Bayes, SVM doesnt need much training data to start providing accurate results. SVM does, however, require more computational resources than Naive Bayes, but the results are even faster and more accurate.

In short, SVM draws a line or hyperplane that divides a space into two subspaces. One subspace contains vectors (tags) that belong to a group, and another subspace contains vectors that do not belong to that group.

But thats the great thing about SVM algorithms theyre multi-dimensional. So, the more complex the data, the more accurate the results will be. Imagine the above in three dimensions, with an added Z-axis, to create a circle.

Deep learning is a set of algorithms and techniques inspired by how the human brain works, called neural networks. Deep learning architectures offer huge benefits for text classification because they perform at super high accuracy with lower-level engineering and computation.

Deep learning is hierarchical machine learning, using multiple algorithms in a progressive chain of events. Its similar to how the human brain works when making decisions, using different techniques simultaneously to process huge amounts of data.

Deep learning algorithms do require much more training data than traditional machine learning algorithms (at least millions of tagged examples). However, they dont have a threshold for learning from training data, like traditional machine learning algorithms, such as SVM and NBeep learning classifiers continue to get better the more data you feed them with:

Deep learning algorithms, like Word2Vec or GloVe are also used in order to obtain better vector representations for words and improve the accuracy of classifiers trained with traditional machine learning algorithms.

Hybrid systems combine a machine learning-trained base classifier with a rule-based system, used to further improve the results. These hybrid systems can be easily fine-tuned by adding specific rules for those conflicting tags that havent been correctly modeled by the base classifier.

Cross-validation is a common method to evaluate the performance of a text classifier. It works by splitting the training dataset into random, equal-length example sets (e.g., 4 sets with 25% of the data). For each set, a text classifier is trained with the remaining samples (e.g., 75% of the samples). Next, the classifiers make predictions on their respective sets, and the results are compared against the human-annotated tags. This will determine when a prediction was right (true positives and true negatives) and when it made a mistake (false positives, false negatives).

Its estimated that around 80% of all information is unstructured, with text being one of the most common types of unstructured data. Because of the messy nature of text, analyzing, understanding, organizing, and sorting through text data is hard and time-consuming, so most companies fail to use it to its full potential.

This is where text classification with machine learning comes in. Using text classifiers, companies can automatically structure all manner of relevant text, from emails, legal documents, social media, chatbots, surveys, and more in a fast and cost-effective way. This allows companies to save time analyzing text data, automate business processes, and make data-driven business decisions.

Manually analyzing and organizing is slow and much less accurate. Machine learning can automatically analyze millions of surveys, comments, emails, etc., at a fraction of the cost, often in just a few minutes. Text classification tools are scalable to any business needs, large or small.

There are critical situations that companies need to identify as soon as possible and take immediate action (e.g., PR crises on social media). Machine learning text classification can follow your brand mentions constantly and in real time, so you'll identify critical information and be able to take action right away.

Human annotators make mistakes when classifying text data due to distractions, fatigue, and boredom, and human subjectivity creates inconsistent criteria. Machine learning, on the other hand, applies the same lens and criteria to all data and results. Once a text classification model is properly trained it performs with unsurpassed accuracy.

Text classification can be used in a broad range of contexts such as classifying short texts (e.g., tweets, headlines, chatbot queries, etc.) or organizing much larger documents (e.g., customer reviews, news articles,legal contracts, longform customer surveys, etc.). Some of the most well-known examples of text classification include sentiment analysis, topic labeling, language detection, and intent detection.

Perhaps the most popular example of text classification is sentiment analysis (or opinion mining): the automated process of reading a text for opinion polarity (positive, negative, neutral, and beyond). Companies use sentiment classifiers on a wide range of applications, like product analytics, brand monitoring, market research, customer support, workforce analytics, and much more.

If you see an odd result, dont worry, its just because it hasnt been trained (yet) with similar expressions. For super accurate results trained to the specific language and criteria of your business, follow this quick sentiment analysis tutorial to build a custom sentiment analysis model in just five steps.

Another common example of text classification is topic labeling, that is, understanding what a given text is talking about. Its often used for structuring and organizing data, such as organizing customer feedback by topic or organizing news articles by subject.

Language detection is another great example of text classification, that is, the process of classifying incoming text according to its language. These text classifiers are often used for routing purposes (e.g., route support tickets according to their language to the appropriate team).

Intent detection or intent classification is another great use case for text classification that analyzes text to understand the reason behind feedback. Maybe its a complaint, or maybe a customer is expressing intent to purchase a product. Its used for customer service, marketing email responses, generating product analytics, and automating business practices. Intent detection with machine learning can read emails and chatbot conversations and automatically route them to the correct department.

Try out this email intent classifier thats trained to detect the intent of email replies. It classifies with tags: Interested, Not Interested, Unsubscribe, Wrong Person, Email Bounce, and Autoresponder:

Text classification has thousands of use cases and is applied to a wide range of tasks. In some cases, data classification tools work behind the scenes to enhance app features we interact with on a daily basis (like email spam filtering). In some other cases, classifiers are used by marketers, product managers, engineers, and salespeople to automate business processes and save hundreds of hours of manual data processing.

With the help of text classification, businesses can make sense of large amounts of data using techniques like aspect-based sentiment analysis to understand what people are talking about and how theyre talking about each aspect. For example, a potential PR crisis, a customer thats about to churn, complaints about a bug issue or downtime affecting more than a handful of customers.

Building a good customer experience is one of the foundations of a sustainable and growing company. According to Hubspot, people are 93% more likely to be repeat customers at companies with excellent customer service. The study also unveiled that 80% of respondents said they had stopped doing business with a company because of a poor customer experience.

For instance, text classification is often used for automating ticket routing and triaging. Text classification allows you to automatically route support tickets to a teammate with specific product expertise. If a customer writes in asking about refunds, you can automatically assign the ticket to the teammate with permission to perform refunds. This will ensure the customer gets a quality response more quickly.

Support teams can also use sentiment classification to automatically detect the urgency of a support ticket and prioritize those that contain negative sentiments. This can help you lower customer churn and even turn a bad situation around.

The information gathered is both qualitative and quantitative, and while NPS scores are easy to analyze, open-ended responses require a more in-depth analysis using text classification techniques. Instead of relying on humans to analyze voice of customer data, you can quickly process open-ended customer feedback with machine learning. Classification models can help you analyze survey results to discover patterns and insights like:

Building your first text classifier can help you really understand the benefits of text classification, but before we go into more detail about what MonkeyLearn can do, lets take a look at what youll need to create your own text classification model:

Say you want to predict the intent of chat conversations; youll need to identify and gather chat conversations that represent the different intents you want to predict. If you train your model with another type of data, the classifier will provide poor results.

You can use internal data generated from the apps and tools you use every day, like CRMs (e.g. Salesforce, Hubspot), chat apps (e.g. Slack, Drift, Intercom), help desk software (e.g. Zendesk, Freshdesk, Front), survey tools (e.g. SurveyMonkey, Typeform, Google Forms), and customer satisfaction tools (e.g. Promoter.io, Retently, Satismeter). These tools usually provide an option to export data in a CSV file that you can use to train your classifier.

Reuters news dataset: probably one the most widely used dataset for text classification; it contains 21,578 news articles from Reuters labeled with 135 categories according to their topic, such as Politics, Economics, Sports, and Business.

Amazon Product Reviews: a well-known dataset that contains ~143 million reviews and star ratings (1 to 5 stars) spanning May 1996 - July 2014. You can get an alternative dataset for Amazon product reviews here.

Luckily, many resources can help you during the different phases of the process, i.e. transforming texts into vectors, training a machine learning algorithm, and using a model to make predictions. Broadly speaking, these tools can be classified into two different categories:

Its an ongoing debate: Build vs. Buy. Open-source libraries can perform among the upper echelon of machine learning text classification tools, but theyre costly and time-consuming to build and require years of data science and computer engineering experience.

SaaS tools, on the other hand, require little to no code, are completely scalable and much less costly, as you only use the tools you need. Best of all, most can be implemented right away and trained (often in just a few minutes) to perform just as fast and accurately.

One of the reasons machine learning has become mainstream is thanks to the myriad of open source libraries available for developers interested in applying it. Although they require a hefty data science and machine learning background these libraries offer a fair level of abstraction and simplification. Python, Java, and R all offer a wide selection of machine learning libraries that are actively developed and provide a diverse set of features, performance, and capabilities.

Python is usually the programming language of choice for developers and data scientists who work with machine learning models. The simple syntax, its massive community, and the scientific-computing friendliness of its mathematical libraries are some of the reasons why Python is so prevalent in the field.

Scikit-learn is one of the go-to libraries for general purpose machine learning. It supports many algorithms and provides simple and efficient features for working with text classification, regression, and clustering models. If you are a beginner in machine learning, scikit-learn is one of the most friendly libraries for getting started with text classification, with dozens of tutorials and step-by-step guides all over the web.

NLTK is a popular library focused on natural language processing (NLP) that has a big community behind it. It's super handy for text classification because it provides all kinds of useful tools for making a machine understand text, such as splitting paragraphs into sentences, splitting up words, and recognizing the part of speech of those words.

A modern and newer NLP library is SpaCy, a toolkit with a more minimal and straightforward approach than NLTK. For example, spaCy only implements a single stemmer (NLTK has 9 different options). SpaCy has also integrated word embeddings, which can be useful to help boost accuracy in text classification.

Once you are ready to experiment with more complex algorithms, you should check out deep learning libraries like Keras, TensorFlow, and PyTorch. Keras is probably the best starting point as it's designed to simplify the creation of recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

TensorFlow is the most popular open source library for implementing deep learning algorithms. Developed by Google and used by companies, such as Dropbox, eBay, and Intel, this library is optimized for setting up, training, and deploying artificial neural networks with massive datasets. Although its harder to master than Keras, its the undisputed leader in the deep learning space. A reliable alternative to TensorFlow is PyTorch, an extensive deep learning library primarily developed by Facebook and backed by Twitter, Nvidia, Salesforce, Stanford University, University of Oxford, and Uber.

Another programming language that is broadly used for implementing machine learning models is Java. Like Python, it has a big community, an extensive ecosystem, and a great selection of open source libraries for machine learning and NLP.

CoreNLP is the most popular framework for NLP in Java. Created by Stanford University, it provides a diverse set of tools for understanding human language such as a text parser, a part-of-speech (POS) tagger, a named entity recognizer (NER), a coreference resolution system, and information extraction tools.

Another popular toolkit for natural language tasks is OpenNLP. Created by The Apache Software Foundation, it provides a bunch of linguistic analysis tools useful for text classification such as tokenization, sentence segmentation, part-of-speech tagging, chunking, and parsing.

Weka is a machine learning library developed by the University of Waikato and contains many tools like classification, regression, clustering, and data visualization. It provides a graphical user interface for applying Wekas collection of algorithms directly to a dataset, and an API to call these algorithms from your own Java code.

The R language is an approachable programming language that is becoming increasingly popular among machine learning enthusiasts. Historically, it has been most widely used among academics and statisticians for statistical analysis, graphics representation, and reporting. According to KDnuggets, its currently the second most popular programming language for analytics, data science, and machine learning (while Python is #1).

Caret is a comprehensive package for building machine learning models in R. Short for Classification and Regression Training, it offers a simple interface for applying different algorithms and contains useful tools for text classification, like pre-processing, feature selection, and model tuning.

Open source tools are great, but they are mostly targeted at people with a background in machine learning. Also, they dont provide an easy way to deploy and scale machine learning models, clean and curate data, tag training examples, do feature engineering, or bootstrap models.

Well, if you want to avoid these hassles, a great alternative is to use a Software as a Service (SaaS) for text classification which usually solves most of the problems mentioned above. Another advantage is that they dont require machine learning experience and even people who dont know how to code can use and consume text classifiers. At the end of the day, leaving the heavy lifting to a SaaS can save you time, money, and resources when implementing your text classification system.

The best way to learn about text classification is to get your feet wet and build your first classifier. If you dont want to invest too much time learning about machine learning or deploying the required infrastructure, you can use MonkeyLearn, a platform that makes it super easy to build, train, and consume text classifiers. And once youve built your classifier, you can see your results in striking detail with MonkeyLearn Studio. Sign up for free and build your own classifier following these four simple steps:

Next, youll need to upload the data that you want to use as examples for training your model. You can upload a CSV or Excel file or import your text data directly from a 3rd party app such as Twitter, Gmail, Zendesk, or RSS feeds:

Once the classifier has been trained, incoming data will be automatically categorized into the tags you specify in this step. Try avoiding using tags that are overlapping or ambiguous as this can cause confusion and can make the classifiers accuracy worse.

As you tag data, the classifier will learn to recognize similar patterns when presented with new text and make an accurate classification. Remember: the more data you tag, the more accurate the model will be.

Now that youve built a classifier, its time to make your results shine in vivid visual detail. Business intelligence visualization platforms allow you to see a broad data overview or fine-grained results.

MonkeyLearn Studio is an all-in-one text data analysis and visualization tool. Choose the classification (and other) techniques you need and perform them together from data collection, to organization, analysis, and visualization. It all works in a single, seamless interface.

Take a look at the example below, where we performed aspect-based sentiment analysis on customer reviews of Zoom. Each piece of feedback is categorized by Usability, Support, Reliability, etc., then sentiment analyzed to show the opinion of the writer.

Text classification can be your new secret weapon for building cutting-edge systems and organizing business information. Turning your text data into quantitative data is incredibly helpful to get actionable insights and drive business decisions. Also, automating manual and repetitive tasks will help you get more done.

Are you interested in creating your first text classifier? Visit MonkeyLearn and start experimenting right away. You can quickly create text classifiers with machine learning by using our easy-to-use UI (no coding required!) and put them to work by using our API or integrations.

nlp - which classifier to choose in nltk - stack overflow

nlp - which classifier to choose in nltk - stack overflow

I want to classify text messages into several categories like, "relation building", "coordination", "information sharing", "knowledge sharing" & "conflict resolution". I am using NLTK library to process these data. I would like to know which classifier, in nltk, is better for this particular multi-class classification problem.

Naive Bayes is the simplest and easy to understand classifier and for that reason it's nice to use. Decision Trees with a beam search to find the best classification are not significantly harder to understand and are usually a bit better. MaxEnt and SVM tend be more complex, and SVM requires some tuning to get right.

With your problem, I would focus first on ensuring you have a good training/testing dataset and also choose good features. Since you are asking this question you haven't had much experience with machine learning for NLP, so I'd say start of easy with Naive Bayes as it doesn't use complex features- you can just tokenize and count word occurrences.

Yes, Training a Naive Bayes Classifier for each category and then labeling each message to a class based on which Classifier provides the highest score is a standard first approach to problems like this. There are more sophisticated single class classifier algorithms which you could substitute in for Naive Bayes if you find performance inadequate, such as a Support Vector Machine ( Which I believe is available in NLTK via a Weka plug in, but not positive). Unless you can think of anything specific in this problem domain that would make Naieve Bayes especially unsuitable, its ofen the go-to "first try" for a lot of projects.

The other NLTK classifier I would consider trying would be MaxEnt as I believe it natively handles multiclass classification. (Though the multiple binary classifer approach is very standard and common as well). In any case the most important thing is to collect a very large corpus of properly tagged text messages.

If by "Text Messages" you are referring to actual cell phone text messages these tend to be very short and the language is very informal and varied, I think feature selection may end up being a larger factor in determining accuracy than classifier choice for you. For example, using a Stemmer or Lemmatizer that understands common abbreviations and idioms used, tagging part of speech or chunking , entity extraction, extracting probably relationships between terms may provide more bang than using more complex classifiers.

This paper talks about classifying Facebook status messages based on sentiment, which has some of the same issues, and may provide some insights into this. The links is to a google cache because I'm having problems w/ the original site:


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