The PARSEME shared task data is a collection of corpora in 18 languages with annotation of verbal multiword expressions (VMWEs) in running texts. VMWEs include idioms (ID, let the cat out of the bag), light verb constructions (LVC, make a decision), verb-particle constructions (VPC, give up), and inherently reflexive verbs (IReflV, se suicider 'to suicide' in French; freuen sich `to be glad' in German), and other phenomena (OTH). For most languages, parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided.

Parseme MWE 1.0 data is available from the Lindat repository, and can be searched through both KonText and NoSke. Detailed information about the annotations can be found at the project documentation page; on this page, we document the way the data has been converted for KonText/NoSke and some of the possibilities how it can be queried.


The data contains the following attributes:

wordthe surface form of the token
lcsurface form, lowercased
lemmathe lemma
lemma_lclemma, lowercased
idnumerical id of the token, unique within sentence
upostag and xpostagpart of speech
featsmorphological features
headid of the syntactic head of the token (possibly multivalue in case of a multi-word token)
deprelthe dependency relation binding the token to its syntactic head
miscany other annotation
mwethe type of MWE (ID,LVC,VPC,IReflV,ID, or OTH) or _ if the token is not part of an MWE
mwe_orderfirst for the first token in an MWE, cont for all remaining tokens
mwe_order_newfirst for the first token in an MWE, last for the last token, cont for any MWE tokens in between
mwe_ida numerical value identifying all tokens belonging to the same MWE, unique within a sentence
mwe_lemmaa concatenation of the lemmas of the tokens belonging to an MWE in the order in which they appear in the sentence

Note that although the names of the attributes are the same as in the Universal Dependencies, some corpora do not use the UD tagsets. All of the attributes except for word and lc may take multiple values: feats is multi-value by design, lemma, id, upostag, xpostag, head and deprel take multiple values only in case of a multi-word token such as the Spanish del = de+el, and the MWE attributes take multiple values in case that the token is simultaneously part of multiple MWEs. Queries for overlapping MWEs demonstrate some possible uses of multivalue attributes.

Sample queries

Here we list a collection of queries that demonstrate some of the abilities of the CQL query type, which is the most expressive query type available in Kontext. We concentrate on the annotation of multiword expressions (MWEs). We link directly to ready-made queries in three common European languages, but the same queries are valid for all 15 languages that can be searched through KonText.
3 languages, Bulgarian, Hebrew and Lithuanian, were not included because the Parseme data does not contain any morpho-syntactic annotation; they may be added upon request.


Simplest queries
[mwe_order="first"] French German Spanish

Search for the first word of each MWE annotated in the corpus. If the same word happens to be the first word of several MWEs, it will appear in the KWIC output only once.

[mwe="LVC"] French German Spanish

Find tokens annotated as part of a light verb construction (LVC).

[lemma="faire" & mwe!="_"] French  German  Spanish

Find a particular verb annotated with any category of VMWE.

[mwe_lemma="faire partie"] French  German  Spanish

Search by a concatenation of the lemmas of words belonging to the MWE (in the order in which they appear in the text).

[mwe="LVC" & upostag="VERB"] French German Spanish

Display LVC tokens that are verbs.

Highlighting multiple words
[mwe_order="first"][mwe_order="cont"]{1,} French German Spanish

Display and highlight continuous MWEs (with no intermediate words between their tokens). The output may possibly contain a discontinuous MWE of three or more words such that the first two directly follow each other. Only the beginning continuous part will be highlighted. Also note than in case of a continuous MWE with three or more words, the KWIC contains multiple lines—one with the first two tokens highlighted, another one with three tokens highlighted. If you need to filter the output to contain only one instance in such cases (with the maximum possible number of words highlighted), please use the instance of the data running in NoSke and use the "Filter > Sub-hits" option.

 1:[mwe_order_new="first"] [mwe_order_new="cont"]* 2:[mwe_order_new="last"] & 1.mwe_id=2.mwe_id within <s/> French German Spanish

Matches whole continuous MWEs if they contain at least two words.

 1:[mwe_order_new="first"] []* 2:[mwe_order_new="last"] & 1.mwe_id=2.mwe_id within <s/> French German Spanish

Matches the first and last words of the MWE together with any words lying between them.

1:[mwe_order="first"] []* 2:[mwe_order="cont"] []* 3:[mwe_order="cont"] & 1.mwe_id=2.mwe_id & 1.mwe_id=3.mwe_id within <s/> French German Spanish

Highlight three tokens in an MWE, including any discontinuities between them.

 Queries for overlapping MWEs
[mwe=".*;.*"] French  German  Spanish

Find tokens that are part of more than one MWE.

[mwe="(.*);\1"] French German

Find tokens that are part of two or more MWEs of the same type; this could be a result of coordination such as in they were going in and out. The Spanish data does not contain any such nodes.

[mwe="ID" & mwe="LVC"] French German Spanish

Find words which are simultaneously part of an idiom (ID) and a light-verb construction (LVC). Formulating the query as [mwe="ID;LVC"] would leave out the words for which the attribute has value LVC;ID or even ID;LVC;LVC. Examples for German and Spanish use a different pair of attribute values.

1:[mwe_order="first" & upostag="VERB" & mwe="LVC"] []* 2:[mwe_order="cont" & upostag="NOUN"] & 1.mwe_id=2.mwe_id within <s/> French German Spanish

Find light verb constructions where the real syntactic head goes first.

 Queries that DO NOT WORK
We apologize that we have previously suggested using the following two queries. Queries mixing the meet operator and global conditions with named tokens (those marked with 1:, 2: etc.) should in fact be avoided. The reason is that (meet 1:[] 2:[] min max) is evaluated first; for each token 1 such that the corresponding token 2 exists, only one such pair is propagated to the evaluation of the global condition (e.g. & 1.mwe_id=2.mwe_id) — if several pairs satisfying the conditions specified inside meet exist, there is no guarrantee that exactly the pair that additionally satisfies the global condition will be propagated and not some other pair (that is then pruned when the global condition is applied). A more detailed discussion of this issue and some examples of the unintuitive results it leads to can be found at #164.
(meet 1:[mwe_order="first"] 2:[mwe_order="cont"] 0 5) & 1.mwe_id=2.mwe_id within <s/> French German Spanish

The first word of the MWE becomes the KWIC; a continuation word is also highlighted, but is not part of the KWIC.

(meet 1:[mwe_id="(.*;.*)"] 2:[] 1 5) & 1.mwe_id=2.mwe_id within <s/> French German Spanish

Find two tokens that simultaneously belong to the same pair of overlapping MWEs. For each token belonging to two MWEs such that there is a token belonging to the same MWEs to the right of it, only the nearest such token is highlighted.



The Spanish, French and Portuguese corpora contain metainformation in the form of sentence ids. Besides that, portions of the data are marked as train or test data in the value of the attribute Metainformation can be viewed in several different ways:

Get involved

Post your own queries at the dedicated google group forum or report any issues or suggestions through the issue tracker.