SHALMANESER

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SHALMANESER is a SHALlow seMANtic parSER.

The name Shalmaneser is borrowed from John Brunner. He describes in his novel "Stand on Zanzibar" an all knowing supercomputer baptized Shalmaneser.

Shalmaneser also has other origins like the king Shalmaneser III.

"SCANALYZER is the one single, the ONLY study of the news in depth that’s processed by General Technics’ famed computer Shalmaneser, who sees all, hears all, knows all save only that which YOU, Mr. and Mrs. Everywhere, wish to keep to yourselves."
John Brunner (1968) "Stand on Zanzibar"

But Shalmaneser is a Micryogenic® computer bathed in liquid helium and it’s cold in his vault.
John Brunner (1968) "Stand on Zanzibar"

“Of course not. Shalmaneser’s main task is to achieve the impossible again, a routine undertaking here at GT.”
John Brunner (1968) "Stand on Zanzibar"

“They programmed Shalmaneser with the formula for this stiffener, see, and…”
John Brunner (1968) "Stand on Zanzibar"

What am I going to do now?
“All right, Shalmaneser!”
John Brunner (1968) "Stand on Zanzibar"

Shalmaneser is a Micryogenic® computer bathed in liquid helium and there’s no sign of Teresa.
John Brunner (1968) "Stand on Zanzibar"

Bathed in his currents of liquid helium, self-contained, immobile, vastly well informed by every mechanical sense: Shalmaneser.
John Brunner (1968) "Stand on Zanzibar"

Please be careful, the whole thing is under construction! For now Shalmaneser it not intended to run on Windows systems since it heavily uses system calls for external invocations. Current versions of Shalmaneser have been tested on Linux only (other *NIX testers are welcome!).

Shalmaneser is a supervised learning toolbox for shallow semantic parsing, i.e. the automatic assignment of semantic classes and roles to text. This technique is often called SRL (Semantic Role Labelling). The system was developed for Frame Semantics; thus we use Frame Semantics terminology and call the classes frames and the roles frame elements. However, the architecture is reasonably general, and with a certain amount of adaption, Shalmaneser should be usable for other paradigms (e.g., PropBank roles) as well. Shalmaneser caters both for end users, and for researchers.

For end users, we provide a simple end user mode which can simply apply the pre-trained classifiers for English (FrameNet 1.3 annotation / Collins parser) and German (SALSA 1.0 annotation / Sleepy parser).

We'll try to provide newer pretrained models for English, German, and possibly other languages as soon as possible.

For researchers interested in investigating shallow semantic parsing, our system is extensively configurable and extendable.

The original version of Shalmaneser was written by Sebastian Padó, Katrin Erk, Alexander Koller, Ines Rehbein, Aljoscha Burchardt and others during their work in the SALSA Project.

You can find original versions of Shalmaneser up to 1.1 on the SALSA project page.

• K. Erk and S. Padó: Shalmaneser - a flexible toolbox for semantic role assignment. Proceedings of LREC 2006, Genoa, Italy. Click here for details.

• TODO: add other works

The project documentation can be found in our doc folder.

We are working now only on the master branch. For different intermediate versions see corresponding tags.

See the installation instructions in the doc folder.

• Ucto

• TreeTagger

• TreeTagger

• BerkeleyParser
• Stanford Parser
• Collins Parser

• OpenNLP MaxEnt
• Mallet

Shalmaneser is released under the GPL v. 2.0 license as of the initial authors.

For a local copy of the full license text see the LICENSE file.

Feel free to contact me via Github. Open an issue if you see problems or need help.