Free wordnet similarity software for linux

WordNet::Similarity is a collection of Perl modules for computing measures of semantic relatedness.

SYNOPSIS

Basic Usage Example

use WordNet::QueryData;

use WordNet::Similarity::path;

my $wn = WordNet::QueryData->new;

my $measure = WordNet::Similarity::path->new ($wn);

my $value = $measure->getRelatedness("car#n#1", "bus#n#2");

my ($error, $errorString) = $measure->getError();

die $errorString if $error;

print "car (sense 1) bus (sense 2) = $valuen";

Using a configuration file to initialize the measure

use WordNet::Similarity::path;

my $sim = WordNet::Similarity::path->new($wn, "mypath.cfg");

my $value = $sim->getRelatedness("dog#n#1", "cat#n#1");

($error, $errorString) = $sim->getError();

die $errorString if $error;

print "dog (sense 1) cat (sense 1) = $valuen";

Printing traces

print "Trace String -> ".($sim->getTraceString())."n";

Introduction

We observe that humans find it extremely easy to say if two words are related and if one word is more related to a given word than another. For example, if we come across two words, car and bicycle, we know they are related as both are means of transport. Also, we easily observe that bicycle is more related to car than fork is. But is there some way to assign a quantitative value to this relatedness? Some ideas have been put forth by researchers to quantify the concept of relatedness of words, with encouraging results.

Eight of these different measures of relatedness have been implemented in this software package. A simple edge counting measure and a random measure have also been provided. These measures rely heavily on the vast store of knowledge available in the online electronic dictionary -- WordNet. So, we use a Perl interface for WordNet called WordNet::QueryData to make it easier for us to access WordNet. The modules in this package REQUIRE that the WordNet::QueryData module be installed on the system before these modules are installed.

WordNet::Similarity::PathFinder is a Perl module to implement path finding methods (by node counting) for WordNet::Similarity measures of semantic relatedness.

SYNOPSIS

use WordNet::QueryData;

my $wn = WordNet::QueryData->new;

use WordNet::Similarity::PathFinder;

my $obj = WordNet::Similarity::PathFinder->new ($wn);

my $result = $obj->parseWps($wps1, $wps2);

my @paths = $obj->getShortestPath("dog#n#1", "cat#n#1", "n", "wps");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

my @paths = $obj->getAllPaths("worship#v#1", "adore#v#1", "v", "wps");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

my @paths = $obj->getShortestPath("02895418", "02724985", "n", "offset");

my ($length, $path) = @{shift @paths};

defined $path or die "No path between synsets";

Introduction

This class is derived from (i.e., is a sub-class of) WordNet::Similarity.

The methods in this module are useful for finding paths between concepts in WordNets is-a taxonomies. Concept A is-a concept B if, and only if, B is a hypernym of A or A is in the hypernym tree of B. N.B., only nouns and verbs have hypernyms.

The methods that find path lengths (such as getShortestPath() and getAllPaths() compute the lengths using node-counting not edge-counting. In general, the length of a path using node-counting will always be one more than the length using edge-counting. For example, if concept A is a hyponym of concept B, then the path length between A and B using node-counting is 2, but the length using edge-counting is 1. Likewise, the path between A and A is 1 using node-counting and 0 using edge-counting.

WordNet project is a large lexical database of English, developed under the direction of George A. Miller. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.
The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNets structure makes it a useful tool for computational linguistics and natural language processing.
Enhancements:
- Major feature enhancements

CLSimilarImages is a command-line utility for finding visually similar images which may not be identical. It reads filenames from standard input, and outputs customisable results to standard output.

It can write similarity data to files for rapid future comparisons, with timestamps for quick updates. The project can compare one collection of images with itself, or two collections with each other. It works well in scripts or with pipes.

FrameNet::WordNet::Detour is a WordNet to FrameNet Detour.

SYNOPSIS

use FrameNet::WordNet::Detour;

# Creation without parameters, $WNHOME and $FNHOME will be used
my $detour = FrameNet::WordNet::Detour->new;

# Creation with some parameters
my $detour2 = FrameNet::WordNet::Detour->new(-wnhome => /path/to/WordNet,
-fnhome => /path/to/FrameNet
-cached => 1,
-limited => undef);

my $result = $detour->query("walk#v#1");

if ($result->is_ok) {
print "Best frames: n";
print join( , @{$result->get_best_framenames})."n";

print "All frames: n";
print join( , @{$result->get_all_framenames})."n";

print "All frames with weights: n";
foreach my $frame (@{$result->get_all_frames}) {
print $frame->name.": ";
print $frame->get_weight."n";
}
} else {
print $result->message."n";
}

Natural Language Toolkit is a suite of Python libraries and programs for symbolic and statistical natural language processing. NLTK includes graphical demonstrations and sample data.
It is accompanied by extensive documentation, including tutorials that explain the underlying concepts behind the language processing tasks supported by the toolkit.
Documentation:
A substantial amount of documentation about how to use NLTK is available from the nltk home page:
< http://nltk.sourceforge.net >
In particular, the NLTK home page contains three types of documentation:
- Tutorials teach students how to use the toolkit, in the context of performing specific tasks. They are appropriate for anyone who wishes to learn how to use the toolkit.
< http://nltk.sourceforge.net/tutorial/ >
- The toolkits reference documentation describes every module, interface, class, method, function, and variable in the toolkit. This documentation should be useful to both users and developers.
< http://nltk.sourceforge.net/ref/nltk.html >
- A number of technical reports are available. These reports explain and justify the toolkits design and implementation. They are used by the developers of the toolkit to guide and document the toolkits construction. Students can consult these reports if they would like further information about how the toolkit is designed and why it is designed that way.
< http://nltk.sourceforge.net/tech/ >
Enhancements:
Code (major):
- changed package name to nltk
- import all top-level modules into nltk, reducing need for import statements
- reorganization of sub-package structures to simplify imports
- new featstruct module, unifying old featurelite and featurestructure modules
- FreqDist now inherits from dict, fd.count(sample) becomes fd[sample]
- FreqDist initializer permits: fd = FreqDist(len(token) for token in text)
- made numpy optional
Code (minor):
- changed GrammarFile initializer to accept filename
- consistent tree display format
- fixed loading process for WordNet and TIMIT that prevented code installation if data not installed
- taken more care with unicode types
- incorporated pcfg code into cfg module
- moved cfg, tree, featstruct to top level
- new filebroker module to make handling of example grammar files more transparent
- more corpus readers (webtext, abc)
- added cfg.covers() to check that a grammar covers a sentence
- simple text-based wordnet browser
- known bug: parse/featurechart.py uses incorrect apply() function
Corpora:
- csv data file to document NLTK corpora
Contrib:
- added Glue semantics code (contrib.glue, by Dan Garrette)
- Punkt sentence segmenter port (contrib.punkt, by Willy)
- added LPath interpreter (contrib.lpath, by Haejoong Lee)
- extensive work on classifiers (contrib.classifier*, Sumukh Ghodke)
Tutorials:
- polishing on parts I, II
- more illustrations, data plots, summaries, exercises
- continuing to make prose more accessible to non-linguistic audience
- new default import that all chapters presume: from nltk.book import *
Distributions:
- updated to latest version of numpy
- removed WordNet installation instructions as WordNet is now included in corpus distribution
- added pylab (matplotlib)
Enhancements:
Code:
- changed nltk.__init__ imports to explicitly import names from top-level modules
- changed corpus.util to use the rb flag for opening files, to fix problems reading corpora under MSWindows
- updated stale examples in engineering.txt
- extended feature stucture interface to permit chained features, e.g. fs[F,G]
- further misc improvements to test code plus some bugfixes
Tutorials:
- rewritten opening section of tagging chapter
- reorganized some exercises

KolourPaint is an easy-to-use paint program for KDE. Features include undo/redo, more than a dozen tools, selections, transparent image editing and zoom support (with an optional grid and thumbnail).
KolourPaint is a free, easy-to-use paint program for KDE.
It aims to be conceptually simple to understand; providing a level of functionality targeted towards the average user. KolourPaint is designed for daily tasks like:
- Painting - drawing diagrams and "finger painting"
- Image Manipulation - editing screenshots and photos; applying effects
- Icon Editing - drawing clipart and logos with transparency
Its not an unusable and monolithic program where simple tasks like drawing lines become near impossible. Nor is it so simple that it lacks essential features like Undo/Redo.
The main difference between KolourPaint and most other "simple" UNIX paint programs is that KolourPaint actually works. See the Product Comparison page for details.
KolourPaint is opensource software written in C++ using the Qt and KDE libraries.
Main features:
- Undo/Redo Support (10-500 levels of history depending on memory usage)
Tools (single key shortcuts available for all tools)
- Brush, Color Eraser, Color Picker, Connected Lines a.k.a. Polyline
- Curve, Ellipse, Eraser, Flood Fill, Line, Pen, Polygon, Rectangle
- Rounded Rectangle, Spraycan, Text
Selections (fully undo- and redo-able)
- Rectangular, Elliptical, Free-Form shapes
- Choice between Opaque and Transparent selections
- Full Clipboard/Edit Menu support
- Freehand resizeable
- Colour Similarity means that you can fill regions in dithered images and photos
Transparency
- Draw transparent icons and logos on a checkerboard background
- All tools can draw in the "Transparent Colour"
Image Effects
- Autocrop / Remove Internal Border
- Balance (Brightness, Contrast, Gamma)
- Clear, Emboss, Flatten, Flip, Invert (with choice of channels)
- Reduce Colours, Reduce to Greyscale, Resize, Rotate
- Scale, Set as Image (Crop), Skew, Smooth Scale, Soften & Sharpen
Close-up Editing
- Zoom (from 0.01x to 16x)
- Grid
- Thumbnail
File Operations
- Open/Save in all file formats provided by KImageIO (PNG, JPEG, BMP, ICO, PCX, TIFF,...) with preview
- Print, Print Preview
- Mail
- Set as Wallpaper

osdchat is a simple messaging utility that allows you to remotely send messages directly to the screen as an on-screen display (OSD).

This may be used a lightweight messaging service in a local area network to message busy admins or communicate with friends who like to watch fullscreen movies or otherwise cannot see whats going on in the background.

There is a wide assortment of options to customize the look, feel, and location of the incoming messages.

I suppose it also shares some similarity with the ancient UNIX utility write, except that tty has been replaced with X. osdchat has been tested only under Linux, specifically Gentoo 1.4 and Slackware 9.0/1, but should work on any full distribution meeting the dependencies.

Installlation:

tar zxvf osdchat-[version].tar.gz
cd osdchat-[version]

Edit the Makefile as needed to change the install paths.

make check (md5sum check to make sure files are intact)
make
make install (need to be root, or have proper capabilities)

Copy sample osdchatd.conf configuration file to /etc or rename to .osdchatd and place it in your home directory. Change settings as desired.

The sample config file is equivalent to the built-in defaults, so a config file is not needed to just test the package.