Getting Started with Carrot2 — Installation to First ClustersCarrot2 is an open-source framework for automatic clustering of small collections of documents, primarily designed to organize search results and text snippets into thematic groups. It supports multiple clustering algorithms, offers a modular architecture, and provides both a Java-based library and several ready-to-run applications (desktop, web, and REST). This guide walks you from installation to producing your first meaningful clusters, with practical tips and example code.
What Carrot2 does and when to use it
Carrot2 groups similar documents or search results into labeled clusters so users can explore large sets of short texts quickly. Typical use cases:
- Organizing search engine result pages (SERPs) into topical buckets.
- Summarizing and grouping short text snippets or news headlines.
- Rapid exploratory analysis of small to medium text corpora.
- Backend services that need lightweight, interpretable clustering.
Carrot2 excels when documents are short and when you want readable cluster labels. For very large datasets or deep semantic understanding, consider scaling strategies or complementary NLP tools.
Editions and components
Carrot2 is provided as:
- A Java library (core) for embedding clustering into applications.
- A web application (REST + UI) that exposes clustering over HTTP.
- A desktop workbench for interactive exploration.
- Integrations and examples (Solr plugin, Elasticsearch connectors, demos).
This guide focuses on the Java library and the web/REST app for quick experimentation.
Prerequisites
Before installing Carrot2, ensure you have:
- Java 11 or later installed (check with
java -version). - Maven or Gradle if you plan to build from source or integrate the library.
- Basic familiarity with JSON and HTTP if using the REST API.
Installation options
You can use Carrot2 in three main ways:
- Use the standalone web application (quickstart).
- Add the Carrot2 Java libraries to a Maven/Gradle project.
- Run the desktop workbench for interactive clustering.
I’ll cover the first two for most practical scenarios.
Quickstart: Run the Carrot2 web application
The web app is the fastest way to try Carrot2 without writing Java code.
- Download the latest Carrot2 distribution (zip) from the project releases page and extract it.
- Inside the extracted folder locate the
carrot2-webapp.jar(or a similarly named executable jar). - Run:
java -jar carrot2-webapp.jar - By default the web UI is available at http://localhost:8080/ and the REST endpoint at http://localhost:8080/rest
The web UI lets you paste documents, choose algorithms, and visualize clusters. The REST API accepts POST requests with documents in JSON and returns cluster structures.
Example REST request (curl):
curl -X POST 'http://localhost:8080/rest' -H 'Content-Type: application/json' -d '{ "documents":[ {"id":"1","title":"Apple releases new iPhone","snippet":"Apple announced..."}, {"id":"2","title":"Samsung unveils flagship","snippet":"Samsung introduced..."} ], "algorithm":"lingo" }' Using Carrot2 as a Java library
If you want to integrate Carrot2 into an application, add the core dependency to your Maven or Gradle project.
Maven (pom.xml snippet):
<dependency> <groupId>org.carrot2</groupId> <artifactId>carrot2-core</artifactId> <version>4.3.1</version> <!-- use latest stable --> </dependency> Gradle (build.gradle snippet):
implementation 'org.carrot2:carrot2-core:4.3.1' // use latest stable Basic Java example (creating clusters from in-memory documents):
import org.carrot2.clustering.*; import org.carrot2.core.*; import org.carrot2.language.English; import java.util.*; public class Carrot2Example { public static void main(String[] args) { // Initialize controller with default configuration and English language Controller controller = ControllerFactory.createSimple(); List<Document> docs = Arrays.asList( new Document("1", "Apple releases new iPhone", "Apple announced..."), new Document("2", "Samsung unveils flagship", "Samsung introduced...") ); ProcessingResult result = controller.process( DocsBuilder.newBuilder(docs).build(), "lingo" // algorithm id: "lingo", "sse", etc. ); for (Cluster c : result.getClusters()) { System.out.println("Cluster: " + c.getLabel()); for (Document d : c.getDocuments()) { System.out.println(" - " + d.getTitle()); } } controller.shutdown(); } } Notes:
- Choose algorithm by id: “lingo” (concept-based), “kmeans” (classic), “sse”, etc.
- You can tune algorithm parameters through attributes passed to the controller.
Algorithms overview
- Lingo: extracts cluster labels from frequent phrases and uses SVD for concept discovery. Good balance between label quality and cluster coherence.
- KMeans: classic vector-space k-means; simple and scalable but labels may need post-processing.
- Suffix tree / suffix array based algorithms (e.g., STC): good for short repetitive texts.
- SSE (Spherical K-Means/Non-negative Matrix Factorization variants): for alternative grouping strategies.
Choose Lingo for most exploratory tasks where readable labels matter.
Preparing documents for better clusters
- Include meaningful titles or short snippets — Carrot2 uses surface text heavily.
- Normalize text (lowercasing is usually handled automatically).
- Remove boilerplate (navigation, timestamps) to reduce noise.
- Provide a few dozen to a few thousand documents; Carrot2 is tuned for small-to-medium collections.
Example: From search results to clusters
If you have search results (title + snippet + URL), map each result to a Document with id/title/snippet/url. Submit the collection to the controller or REST endpoint and request “lingo”. Carrot2 will return named clusters with scores and document membership.
Typical JSON output includes:
- clusters: list of {label, score, documents: [ids]}
- metadata about processing and used algorithm
Tuning and parameters
Common parameters:
- Minimal cluster size: filter out tiny clusters.
- Number of clusters (for kmeans).
- Labeling thresholds and phrase-length limits.
In Java, set attributes via AttributeNames or a Map when calling controller.process(…). In REST, pass parameters as JSON fields.
Evaluating cluster quality
- Coherence: do documents in a cluster share a clear topic?
- Label accuracy: does the label summarize the member documents?
- Use human evaluation on sample clusters; automated measures (e.g., purity, NMI) require ground truth.
Scaling and production considerations
- For large-scale needs, run Carrot2 as a microservice behind a queue; batch documents into reasonable sizes.
- Cache cluster results for repeated queries.
- Combine Carrot2 with an index (Solr/Elasticsearch) for retrieving documents and then clustering the top-k results.
- Monitor memory and GC: clustering uses vector representations and SVD for some algorithms.
Troubleshooting common issues
- No clusters / weak labels: try Lingo if using kmeans, increase document count, or clean input text.
- OutOfMemoryError: increase JVM heap (-Xmx) or batch documents.
- Slow SVD: reduce dimension or use fewer documents for interactive use.
Further resources
- Official Carrot2 documentation and API docs (check latest release notes).
- Example integrations (Solr plugin) if using search platforms.
- Source code and community forums for advanced customization.
Carrot2 provides a lightweight, practical way to turn lists of short texts into readable clusters quickly. Start with the web app for fast iteration, then embed the Java library when you need integration or customization.
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