This page is intended as a collection of pointers related to using the ERG (for parsing or generation), with an eye towards first-time users. The page was initiated by StephanOepen, who intends to maintain it over time. Seeing that it will be important for the information provided here to be accurate and up-to-date, please be both pedantic and conservative in making (non-trivial) revisions.

Choice of Run-Time Environment

To parse running text using the ERG, a number of tools are required. Basically, there are three possible paths towards a software environment supporting the ERG: (a) a ‘full-blown’ DELPH-IN distribution for Linux; (b) the stand-alone ACE or PET parsers, which can be compiled in various operating environments; and (c) a ‘thin’, portable client against the RESTful on-line interface. Each of these choices has distinct advantages and limitations—discussed briefly in the individual sections below. All three are regularly used and, thus, ‘suported’ by the ERG developers, who will be happy to try and assist with installation and usage questions. Please contact for support.

(A) The LOGON Distribution: Installation

For Linux users, the most straightforward way of installing the full DELPH-IN toolchain is through the so-called LOGON distribution (see the pages LogonTop and LogonInstallation for background). On any reasonably recent Linux distribution (running on 32- or 64-bit x86 derivatives, where 32-bit compatibility libraries need to be available on natively 64-bit environments), do the following (note that the full installation requires a little more than one gigabyte in available disk space, and the process of downloading the full tree can take between ten minutes and a couple of hours):

  svn export logon

In case you do not have access to a suitable Linux installation, are critically short on disk space, or lack the network bandwidth to download multiple gigabytes (but are so fortunate as to have access to a computer and the Internet)—please consider one of the processing options (B) or (C) below (i.e. the stand-alone ACE or PET parsers, or the RESTful on-line interface). Finally, the web-accessible on-line demonstration allows inspection of ERG parsing outputs without software installation; note, however, that comparatively tight limits are imposed on time and memory usage in this interface.


For the full functionality of the LOGON tree, there is a certain amount of first-time configuration, documented as Step (1) on the LogonInstallation page. However, to merely parse a sequence of sentences, it should work to directly move on to just running the system, from the command line:

  cd logon
  ./parse --binary --erg+tnt --best 1 --text ./lingo/erg/etc/test.txt

Here, the file ‘test.txt’ provides a newline-separated list of strings (using Un*x-style line break conventions), where each line will be fed to the parser individually for syntactic analysis (with a standard configuration, handling unknown words on the basis of a PoS tagging pre-processing step and lightweight RE-based named entity detection; for details, see the PetInput page).

If all goes well (as it should), the above command will produce tracing outputs somewhat like the following:

  International Allegro CL Enterprise Edition
  8.2 [64-bit Linux (x86-64)] (Oct 27, 2011 17:11)
  Copyright (C) 1985-2010, Franz Inc., Oakland, CA, USA.  All Rights Reserved.

  This standard runtime copy of Allegro CL was built by:
     [TC13152] Universitetet i Oslo (IFI)

  ; Loading /ltg/oe/src/logon/dot.tsdbrc


  [sh 0.0] (1) `The ERG is easy to install and use .' [100000] --- 1 (0.11|0.10:0.11 s) <58:1058> {2612:4471} (26M).
  [sh 0.0] (2) `Parsing English with the ERG is a real pleasure .' [100000] --- 1 (0.12|0.11:0.12 s) <59:959> {2108:5709} (32M).
  [sh 0.0] (3) `We are grateful to everyone who contributed to the grammar and software .' [100000] --- 1 (0.18|0.16:0.18 s) <84:1777> {4202:8436} (45M).
  [t40002] total elapsed parse time 0.4s; 3 items; avg time per item 0.1333s
  flush-cache(): flushing `erg/1111/test/12-01-17/pet' cache ... done.

The numbers following each parser input on the last few lines above record various statistics, for example: (a) producing a single analysis in all cases (meaning the grammar was able to assign a parse to each of these utterances, and reflecting that the ‘--best 1’ option in our example command asks for only the most probable analysis to be extracted); (b) taking between 110 and 190 miliseconds per sentence; or (c) requiring between 26 and 45 megabytes of dynamic memory while parsing one sentence. More detailed information about the batch parsing process is available through the LogonProcessing/BatchParsing page.

As a side effect of the above run, parsing results and statistics were written into a simple (file-based, textual) database, a so-called competence and performance profile. For our running example, this profile will be located in a newly created sub-directory called erg/1111/test/12-01-17/pet’, within the so-called [incr tsdb()] database home ‘lingo/lkb/src/tsdb/home/’ inside the LOGON tree.

Output Formats

While it is possible to operate directly on the profile directory, it may be more convenient (for non-expert users) to export key elements of each parsing result into a (slightly) more human-readable form. To do so, a command like the following can be be used (where the name of the profile holding parsing results needs to reflect the current date, of course):

  ./redwoods --binary --erg --default --composite --target /tmp \
    --export derivation,tree,mrs,eds --active all \

This command asks to export four distinct views on each analysis: (a) the so-called derivation tree (i.e. the exact HPSG recipe); (b) a simplified syntactic constituent tree (using a set of conventional category labels); (c) a logical-form meaning representation in Minimal Recursion Semantics (MRS); and (d) a reduced variant of the MRS, in the form of elementary semantic dependencies (EDS). These outputs will be available, in a newly created file in the --target’ directory, named after the original parsing profile, i.e. in this case ‘/tmp/’ (note that export files by default are compressed using GNU gzip(1); sample output for our running example is available as a separate ErgProcessing/SampleExport page; formats (a), (c), and (d) are further discussed to some degree on the ItsdbDerivations, ErgSemantics, and EdsTop pages, respectively).

If you like what you are seeing, it is probably about time to read more about the ERG and DELPH-IN technology, for example starting from the ErgTop and LogonTop pages on this wiki, maybe perusing our mailing list archives, or preparing a grant application or donation to work with us on improving the grammar and tools. There are numerous ways of running the toolchain and of adapting the grammar and engine to various subject domains, genres, and more generally to a specific use case. Furthermore, (even) more detailed syntacto-semantic information is available from the full HPSG analyses delivered by the grammar than what is exposed through the four interface representations shown in the above.

ACE and FFTB in the LOGON Distribution

Pre-compiled binaries of the Answer Constraint Engine (ACE) parser–generator and its Full-Forest Treebanker (FFTB) are bundled with the LOGON distribution. As an alternatively to the parsing command suggested above, one can for example provide --erg+tnt/ace as the first argument to the parse script, to invoke the ACE parser. Among the distinctive features of ACE is its efficient implementation of chart realization (i.e. surface generation). Further instructions on how to invoke the ACE parser and full-forest treebanker from within the integrated LOGON distribution are available on the LogonAnswer page.

(B) Stand-Alone Parsing (and Generation) with ACE or PET

The LOGON distribution bundles several of the DELPH-IN tools and grammars, including various interfaces for data in- and output. For ‘core’ parsing or realization functionality using the ERG, the stand-alone Answer Constraint Engine (ACE) parser–generator (implemented in ANSI C), or the PET parser (implemented in ANSI C++) may be sufficient. These tools can in principle be compiled for a wide range of operating environments (with known success stories on Linux and MacOS for ACE, and Linux and Windows for PET), but are also available in pre-compiled form. Furthermore, the stand-alone parsers provide a more light-weight software installation and make available both a command-line and programmatic interface.

For instructions on how to obtain and run ACE, including pre-compiled snapshots of recent ERG releases, please see the AceTop page.

For some advice on using options to improve the utility of the ERG with ACE, please see the AceErgTuning page.

(C) RESTful Interactions with the On-Line ERG Service

As of mid-2016, a programmatic RESTful on-line interface to ERG parsing is available, including a sample ‘thin’ client in Python and integration in the pyDelphin environment. For emerging documentation on this interface, please see the ErgApi page.


The basic functionality of the LOGON tree, as exemplified in the examples above is periodically validated (as long as we recommend everyone use the trunk; more testers would be welcome):

ErgProcessing (last edited 2019-07-19 11:58:57 by DanFlickinger)

(The DELPH-IN infrastructure is hosted at the University of Oslo)