Semantic Models Package (SEMMOD)

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	By default SEMMOD comes with C modules compiled for 32-bit Linux systems. If you are running a 64-bit system then you will need to run the setup.py scripts in the following directories with the command line "python setup.py":		By default SEMMOD comes with C modules compiled for 32-bit Linux systems. If you are running a 64-bit system then you will need to run the setup.py scripts in the following directories with the command line "python setup.py":
-	''[your install directory]/semmod-1.1/semmod/spsvd/setup.py''	+	''[your install directory]/semmod-1.3/semmod/spsvd/setup.py''
-	''[your install directory]/semmod-1.1/semmod/csm/setup.py''	+	''[your install directory]/semmod-1.3/semmod/csm/setup.py''
-	''[your install directory]/semmod-1.1/semmod/spnmf/setup.py''	+	''[your install directory]/semmod-1.3/semmod/spnmf/setup.py''
-	''[your install directory]/semmod-1.1/semmod/topics/setup.py''	+	''[your install directory]/semmod-1.3/semmod/topics/setup.py''
	Note: Do this before you run the main installation script contained in:		Note: Do this before you run the main installation script contained in:
-	''[your install directory]/semmod-1.1/setup.py''	+	''[your install directory]/semmod-1.3/setup.py''
	== Papers ==		== Papers ==

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Current revision

Contents 1 Background 2 SEMMOD Prerequisites 3 Installation Problems 4 Papers 5 Spaces

Background

In both intelligence and command and control operations the ability to identify and process natural language is pivotal. The task is made difficult by the volume of such information available making automated methods important in narrowing the search for crucial information. Unlike existing search engine technologies that are successful on the world wide web, emphasis must be placed not only on the precision of retrieved results, but also on recall. There are a number of methods for extracting semantic information that have been introduced in recent years that have yet to be compared systematically in military-like contexts. In this package we implement some of the more prominent methods, in preparation for there use in a systematic comparison. The methods covered are:

  1. Vector Space Model (Salton, Wong & Yang, 1975)
  2. Latent Semantic Analysis (Martin & Berry, 2007)
  3. the topics model (Griffiths & Steyvers, 2002)
  4. Non-negative matrix factorization (Lee & Seung, 1999, Ge & Iwata, 2002)
  5. Sparse Non-negative matrix factorization (Shashua & Hazan, 2005)
  6. Independent Components Analysis (Isbell & Viola 1998)
  7. Sparse ICA (Bronstein, Bronstein, Zibulevsky & Zeevi, 2005)
  8. Syntagmatic Paradigmatic model (Dennis, 2005)
  9. Constructed Semantics Model (Kwantes, 2005)

SEMMOD Prerequisites

SEMMOD has been tested under 32-bit and 64-bit versions of both Windows and Linux (Ubuntu) operating systems under is released under the GNU General Public License. The SEMMOD package is primarily written in Python, however sections have been optimized with C to enable timely compilation of model spaces. Semmod also relies on the Numpy and Scipy packages to implement matrix calculations.

  1. SEMMOD Package (Windows & Linux) - (http://www.psychology.adelaide.edu.au/mall_lab/semmod.tar.gz)
  2. Python 2.5 - (http://www.python.org/)
  3. Numpy 1.0.1 - (http://numpy.scipy.org/)
  4. Scipy 0.5.2 -(http://www.scipy.org/)
  
  Note: While SEMMOD was originally written using the aforementioned versions of Numpy and Scipy,
  newer versions of these package also work however they create some deprecated warnings that can be 
  safely ignored when running SEMMOD.

Installation Problems

Running 64-bit Linux?

By default SEMMOD comes with C modules compiled for 32-bit Linux systems. If you are running a 64-bit system then you will need to run the setup.py scripts in the following directories with the command line "python setup.py":

  [your install directory]/semmod-1.3/semmod/spsvd/setup.py
  [your install directory]/semmod-1.3/semmod/csm/setup.py
  [your install directory]/semmod-1.3/semmod/spnmf/setup.py
  [your install directory]/semmod-1.3/semmod/topics/setup.py

Note: Do this before you run the main installation script contained in:

  [your install directory]/semmod-1.3/setup.py

Papers

Stone, B., Dennis, S. & P. J. Kwantes (2008). A Systematic Comparison of Semantic Models on Human Similarity Rating Data: The effectiveness of subspacing. The Proceedings of the Thirtieth Conference of the Cognitive Science Society.. [PDF].

Spaces