Link Grammar Parser


September, 2021: link-grammar 5.10.2 released! See below for a description of recent changes.

What is Link Grammar?

The Link Grammar Parser exhibits the linguistic (natural language) structure of English, Russian, Arabic, Persian and limited subsets of a half-dozen other languages. This structure is a graph of typed links (edges) between the words in a sentence. One may obtain the more conventional HPSG (constituent) and dependency style parses from Link Grammar by applying a collection of rules to convert to these different formats. This is possible because Link Grammar goes a bit "deeper" into the "syntactico-semantic" structure of a sentence: it provides considerably more fine-grained and detailed information than what is commonly available in conventional parsers.

The theory of Link Grammar parsing was originally developed in 1991 by Davy Temperley, John Lafferty and Daniel Sleator, at the time professors of linguistics and computer science at the Carnegie Mellon University. The three initial publications on this theory provide the best introduction and overview; since then, there have been hundreds of publications further exploring, examining and extending the ideas.

Although based on the original Carnegie-Mellon code base, the current Link Grammar package has dramatically evolved and is profoundly different from earlier versions. There have been innumerable bug fixes; performance has improved by more than an order of magnitude. The package is fully multi-threaded, fully UTF-8 enabled, and has been scrubbed for security, enabling cloud deployment. Parse coverage of English has been dramatically improved; other languages have been added (most notably, Russian). There is a raft of new features, including support for morphology, log-likelihood semantic selection, and a sophisticated tokenizer that moves far beyond white-space-delimited sentence-splitting.

The latest addition is an experimental sentence generator; it is being used in the OpenCog Language Learning project, which aims to automatically learn Link Grammars from corpora, using brand-new and innovative information theoretic techniques, somewhat similar to those found in artificial neural nets (deep learning), but using explicitly symbolic representations.

Quick Overview

The parser includes API's in various different programming languages, as well as a handy command-line tool for playing with it. Here's some typical output:

              linkparser> This is a test!
                 Linkage 1, cost vector = (UNUSED=0 DIS= 0.00 LEN=6)
                  +----->WV----->+---Ost--+   |
                  +---Wd---+-Ss*b+  +Ds**c+   |
                  |        |     |  |     |   |
              LEFT-WALL this.p is.v a  test.n !
              (S (NP this.p) (VP is.v (NP a test.n)) !)
                          LEFT-WALL    0.000  Wd+ hWV+ Xp+
                             this.p    0.000  Wd- Ss*b+
                               is.v    0.000  Ss- dWV- O*t+
                                  a    0.000  Ds**c+
                             test.n    0.000  Ds**c- Os-
                                  !    0.000  Xp- RW+
                         RIGHT-WALL    0.000  RW-

This rather busy display illustrates many interesting things. For example, the Ss*b link connects the verb and the subject, and indicates that the subject is singular. Likewise, the Ost link connects the verb and the object, and also indicates that the object is singular. The WV (verb-wall) link points at the head-verb of the sentence, while the Wd link points at the head-noun. The Xp link connects to the trailing punctuation. The Ds**c link connects the noun to the determiner: it again confirms that the noun is singular, and also that the noun starts with a consonant. (The PH link, not required here, is used to force phonetic agreement, distinguishing 'a' from 'an'). These link types are documented in the English Link Documentation.

The bottom of the display is a listing of the "disjuncts" used for each word. The disjuncts are simply a list of the connectors that were employed to form the links. They are particularly interesting because they serve as an extremely fine-grained form of a "part of speech" or "grammatical category", although they also can be interpreted as "semantic selections". Thus, for example: the disjunct S- O+ indicates a transitive verb: its a verb that takes both a subject and an object. The additional markup above indicates that 'is' is not only being used as a transitive verb, but it also indicates finer details: a transitive verb that took a singular subject, and was used (is usable as) the head verb of a sentence. The floating-point value is the "cost" of the disjunct; it very roughly captures the log-likelihood of this particular grammatical (and semantic!) usage. Much as parts-of-speech correlate with word-meanings, so also fine-grained parts-of-speech correlate with much finer distinctions and gradations of meaning.

The link-grammar parser also supports morphological analysis. Here is an example in Russian:

              linkparser> это теста
                 Linkage 1, cost vector = (UNUSED=0 DIS= 0.00 LEN=4)
                  +---Wd---+       +-LLCAG-+
                  |        |       |       |
              LEFT-WALL это.msi тест.= =а.ndnpi

The LL link connects the stem 'тест' to the suffix 'а'. The MVA link connects only to the suffix, because, in Russian, it is the suffixes that carry all of the syntactic structure, and not the stems. The Russian lexis is documented here.


An extended overview and summary of Link Grammar can be found on the Link Grammar Wikipedia page, which touches on most of the important, primary aspects of the theory. However, it is no substitute for the original papers published on the topic:

A fairly comprehensive bibliography of papers written before 2004 is here and is mirrored here. A sampling of publications that reference Link Grammar in some way can be found here; some of these may be downloaded here.


There is an extensive set of pages documenting the English dictionary; specifically, the names of links and their meanings, as well as how to write new rules. There is also a short primer for creating dictionaries for new languages.

The documentation for the C/C++ programming API is here. Bindings for other programming languages can be found in the bindings directory in the GitHub Link Grammar Repo.

System Summary

Downloading Link Grammar

The source code to the system can be downloaded as a tarball. The current stable version is Link Grammar 5.10.2 (September, 2021). Older versions are available here.

GitHub hosts the primary link-grammar repository. Issues (bugs) should be reported there. Developers who are not a part of the core development team should not use or deploy the source from github. It is unstable and frequently buggy and broken! All users should use the tarballs, only!

Mailing Lists

The mailing list for Link Grammar discussion is at the link-grammar google group.

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Ongoing development by OpenCog

Ongoing development of Link Grammar is guided and supported by the Open Cognition project, where the parser plays an important role in the OpenCog natural language processing subsystem. Research and implementation is ongoing; current work includes investigations into unsupervised learning of language.

Stanford Parser Compatibility

A sibling project, RelEx, uses constraint-grammar-like techniques to extract dependency relations that are compatible with the Stanford parser. It's performance is comparable to the Stanford PCFG parsing model, and is more than three times faster than the Stanford "lexicalized" (factored) model.

The RelEx project is no longer in active development. We learned (the hard way) that the native Link Grammar parses contain much more information than the Stanford dependency markup is capable of supporting. The Stanford-style dependencies are simply are not rich or sophisticated enough to produce the kind of data needed for semantic analysis and comprehension, viz. tasks such as predicate-argument extraction, framing, semantic selection, and the like.

Language generation

For sentence generation, i.e. the creation of grammatically correct sentences from a bag of semantic relations, the microplanner and surface realization (sureal) portion of OpenCog is strongly recommended. A short example is here. These "sort-of work", but not very well. The primary issue is that they do not make use of the statistical information available in language to choose likely or reasonable sentence constructions.

We previously recommended two projects that should now be considered obsolete: NLGen and NLGen2. For your entertainment, they're still listed below: The NLGen and NLGen2 projects provide natural language generation modules, based on, and compatible with link-grammar and RelEx. They implement the SegSim ideas for NL generation. See the following YouTube videos of a virtual dog, showing some of NLGen's capabilities (circa 2009): Demo of Virtual Dog Learning to Play Fetch via Imitation and Reinforcement, AI Virtual Dog's Emotions Fluctuate Based on Its Experiences, Demo of Embodied Anaphora Resolution and AI Virtual Dog Answers Simple Questions about Itself and Its Environment.

Linguistic Disclaimer

Link Grammar is a natural language parser, not a human-level artificial general intelligence. This means that there are many sentences that it cannot parse correctly, or at all. There are entire classes of speech and writing that it cannot handle, including twitter posts, IRC chat logs, Valley-girl basilect, Old and Middle English, stock-market listings and raw HTML dumps.

Link Grammar works best with "newspaper English", as taught to and written by those educated in American colleges: standard-sized sentences, with proper grammar, proper punctuation, and correct capitalization. Link Grammar has difficulties with the following types of textual input:

It is hoped that the unsupervised learning of language proposal will be of sufficient power and ability to handle most of these exceptional cases. Work is currently ongoing.

Natural Language Support

Ranked in order of maturity.

The main English documentation is here.
A set of Russian dictionaries providing full coverage for the language have been incorporated into the main distribution as of version 4.7.10 (March 2013). An older version, from which these are derived, can be found at By Sergey Protasov. Includes link documentation (mirror) and subscript (morphology) documentation (mirror). Russian morpheme dictionaries can be had at

Документация по связям и по классам слов доступна в виде списка примеров.

The Persian dictionaries from Jon Dehdari have been incorporated into the main distribution, as of version 5.0.0 (April 2014). This includes a copy of the Persian stemming engine, as significant morphology analysis needs to be performed to parse Persian.
The Arabic dictionaries from Jon Dehdari have been incorporated into the main distribution, as of version 5.0.0 (April 2014). These are derived from the older, original version. [Mirror] These require the Aramorph stemming package, which is included.
A small German dictionary, consisting of 850 words, is included. A brief description is provided here.
A small Lithuanian prototype dictionary has been created. It contains a few hundred words. A few basic sentences parse just fine; the current version focuses on morphological analysis coupled with grammatical analysis. Documentation is here.

Sukurta yra labai prasta Lietuvių kalbos žodynas; beveik neiks ikį šiol neveikia. Čia dokumentacija.

A small Vietnamese prototype dictionary has been created. It contains several hundred words.
A small Indonesian prototype dictionary has been created. It contains about one hundred words.
A very small Hebrew prototype dictionary has been created. It contains a few dozen words. Almost nothing works correctly (yet).
A very small Kazakh prototype dictionary has been created. It contains a few dozen words. Almost nothing works correctly (yet).
A very small Turkish prototype dictionary has been created. It contains a few dozen words. Almost nothing works correctly (yet).
French, Luthor project
The Luthor project aims to develop a set of scripts to automatically construct Link Grammar linkage dictionaries by mining Wiktionary data. Current efforts are focusing on French. (This project appears to be defunct).

Adjunct Projects

The default distribution for Link Grammar includes bindings for Java, Python, Vala, OCaML, Common Lisp, and AutoIt, as well as a SWIG FFI interface file. Additional language bindings, and some related projects, are listed below:

RelEx Semantic Relation Extractor
RelEx is an English-language semantic relationship extractor, built on the Link Parser. It can identify subject, object, indirect object and many other relationships between words in a sentence. It will also provide part-of-speech tagging, noun-number tagging, verb tense tagging, gender tagging, and so on. RelEx includes a basic implementation of the Hobbs anaphora (pronoun) resolution algorithm.
Ruby bindings
Ruby bindings are coordinated at the Ruby-LinkParser website. The code can be found at the ged/link-parser github page.
Perl bindings
Perl bindings, created by Danny Brian, can be found on the Lingua-LinkParser page on CPAN. Caution: those bindings appear to be unmaintained; currently, they includ features that were removed more than than five years ago. (We encourage a new maintainer to step up!) There is also a tutorial written against a very old version of the bindings; some details may be different.
Psi Toolkit (Perl)
The Psi Toolkit, an NLP toolkit aimed at linguists and NLP engineers, includes bindings for link-grammar, via perl.

Recent Changes

Version 5.10.2 (16 September 2021)

Version 5.10.1 (7 September 2021)

Version 5.10.0 (4 September 2021)

The minor version number has been bumped because of a change to the link types used for idioms. Subscripts with an underbar are now reserved.

Users of the unsupervised language learning project will need this version. It contains fixes for random corpora generation.

Version 5.9.1 (28 April 2021)

Emergency bug fix.

Version 5.9.0 (25 April 2021)

An experimental sentence generator has been added. This generator will create new grammatical sentences, based on a "fill in the blanks" approach to specifying a template sentence. The dictionary is scanned for any suitable words that might fit into wild-card locations; the resulting sentences are then printed. This is particularly useful for generating random corpora of grammatically valid sentences.

Version 5.8.1 (8 January 2021)

Assorted fixes.

Version 5.8.0 (28 February 2020)

Notable changes include: inclusion of javascript node.js bindings; the obsoleting of python2, improved English dictionaries, and most interestingly, an experimental interface for dialects. With this interface, one can provide alternative weightings that emphasize the type of speech that might be common in limited geographical areas, and would not be considered to be commonplace. For example, one can provide weightings for Irish-American, urban English, and newspaper-headline English which might otherwise interfere with ordinary parsing of mainstream English.

Version 5.7.0 (13 September 2019)

This version has one quite remarkable change: the parsing of long sentences has been improved by a factor of 3x or 4x, and thus, the parse speed of many "typical" texts is doubled, or more. Two other important fixes are for broken 32-bit support, and for Windows.

A list of older changes can be found here.


Issues concerning this website should be addressed to Linas Vepstas - <> or Dom Lachowicz - <>.


Current versions of the Link Grammar parser software, language dictionaries and documentation are available under the LGPL v2.1 license. Versions prior to 5.0.0 are available under a variant of the BSD license.

Copyright (c) 2003-2004 Daniel Sleator, David Temperley, and John Lafferty. All rights reserved.
Copyright (c) 2003 Peter Szolovits
Copyright (c) 2004,2012,2013 Sergey Protasov
Copyright (c) 2006 Sampo Pyysalo
Copyright (c) 2007 Mike Ross
Copyright (c) 2008,2009,2010 Borislav Iordanov
Copyright (c) 2008-2021 Linas Vepstas
Copyright (c) 2014-2021 Amir Plivatsky