SENNA

SENNA is a software distributed under a non-commercial license, which outputs a host of Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (NER) and semantic role labeling (SRL).

SENNA is fast because it uses a simple architecture, self-contained because it does not rely on the output of existing NLP system, and accurate because it offers state-of-the-art or near state-of-the-art performance.

SENNA is written in ANSI C, with about 2500 lines of code. It requires about 150MB of RAM and should run on any IEEE floating point computer.

Proceed to the download page. Read the compilation section in you want to compile SENNA yourself. Try out a sanity check. And read about the usage.

SENNA sources are provided so you can adapt SENNA to your needs (under our license constraints), and assess its simplicity.

We also provide binaries for a couple of platforms, in the same archive. The name of the executable for each platform is given as follow:

• Linux 64 bits: senna-linux64
• Linux 32 bits: senna-linux32
• Windows 32 bits: senna-win32.exe
• Mac OS X Leopard & Snow Leopard PPC/Intel 32 bits/Intel 64 bits: senna-osx

Everything is included in a single tar-gzipped file (142MB). Proceed to the download page.

Compiling SENNA is straightforward, as it is written in ANSI C and does not require external libraries. For speed it is however recommended to use the Intel MKL library.

In Linux/Unix/MacOS X systems, use gcc compiler:

gcc -o senna -O3 -ffast-math *.c

You might want to add additional suitable optimization flags for your platform. SENNA also compiles fine with the Intel compiler (icc).

If speed is critical, we recommend to compile SENNA with the Intel MKL library, which provides a very efficient BLAS. Add the definition USE_MKL_BLAS, as well as correct MKL libraries and include path.

gcc -o senna -O3 -ffast-math *.c -DUSE_MKL_BLAS [...]

SENNA also compiles with ATLAS BLAS. On our platform, the handcrafted code compiled with the gcc command line shown above was faster. However, if you want to use it, you can compile it with:

gcc -o senna -O3 -ffast-math *.c -DUSE_ATLAS_BLAS [...]

Assuming you installed the XCode tools (which are provided on the Mac OS X DVD/CDs), simply compile with gcc:

gcc -o senna -O3 -ffast-math *.c -DUSE_APPLE_BLAS -framework Accelerate

This will compile against Apple BLAS libraries included in your system.

SENNA compiles fine under Windows. You will have to create a Win32 console project under Microsoft Visual Studio (you can download the Express Edition). Add all the includes and C file into the project, and build the solution.

We recommend to use Intel MKL for speed. See your MKL manual for adding proper libraries and includes. Add also the preprocessor definition USE_MKL_BLAS in the project.

Run in a console the following command:

senna < sanity-test-input.txt > sanity-test-result.txt

SENNA should create a file sanity-test-result.txt which should be identical to the provided sanity-test-output.txt file.

The file sanity-test-input.txt comes from the CoNLL 2000 chunking testing set. SENNA will output all tags for this file. It should run in about 40 seconds on a recent desktop computer.

SENNA reads input sentences from the standard input and outputs tags into the standard output. The most likely command line usage for SENNA is therefore:

senna [options] < input.txt > output.txt

Of course you can run SENNA in an interactive mode without the "pipes" < and >.

Each input line is considered as a sentence. SENNA has its own tokenizer for separating words, which can be deactivated with the -usrtokens option.

SENNA outputs one line per "token", with all the corresponding tags (in IOBES format) on the same line. An empty line is inserted between each output sentence. The first column is the token. Tags for all task then follow by default (POS, CHK, NER and SRL). Tags for SRL are preceeded by a column which indicates if SENNA considered the token as a SRL verb or not ("-"). Then, there is one column per SRL verb.

SENNA supports the following options:

• -h
  Display an inline help.
• -verbose
  Display model informations (on the standard error output, so it does not mess up the tag outputs).
• -notokentags
  Do not output tokens (first output column).
• -iobtags
  Output IOB tags instead of IOBES.
• -brackettags
  Output 'bracket' tags instead of IOBES.
• -path <path>
  Specify the path to the SENNA data/ and hash/ directories, if you do not run SENNA in its original directory. The path must end by "/".
• -usrtokens
  Use user's tokens (space separated) instead of SENNA tokenizer.
• -posvbs
  Use verbs outputed by the POS tagger instead of SRL style verbs for SRL task. You might want to use this, as the SRL training task ignore some verbs (many "be" and "have") which might be not what you want.
• -usrvbs <file>
  Use user's verbs (given in <file>) instead of SENNA verbs for SRL task. The file must contain one line per token, with an empty line between each sentence. A line which is not a "-" corresponds to a verb.
• -pos
-chk
-ner
-srl
  Instead of outputing tags for all tasks, SENNA will output tags for the specified (one or more) tasks.

We report here SENNA performance in per-word accuracy for POS, and F1 score for all the other tasks.

Task	Benchmark		Performance
Part of Speech (POS)	(Toutanova et al, 2003)	(Accuracy)	97.29%
Chunking (CHK)	CoNLL 2000	(F1)	94.32%
Name Entity Recognition (NER)	CoNLL 2003	(F1)	89.59%
Semantic Role Labeling (SRL)	CoNLL 2005	(F1)	75.49%

Please email to Ronan Collobert for any problem report or positive feedback. We will be glad to hear from you.