UMR Annotation of Multiword Expressions (2023)

Abstract: Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. There are different types of MWEs which require different kinds of annotation that must be specified in guidelines. This paper discusses the specific treatment for each type of MWE in UMR.

## introduction uniform meaning representation (umr) @xcite is a graph-based formalism designed to represent natural language semantics. it is based on abstract meaning representation (amr) @xcite , but is enriched and extended in accordance with typological principles to account for linguistic uniformity and variation across a wide range of languages of the world, from languages like arabic, chinese, and english that have a large population of speakers, to languages like arapaho, kukama, navajo, and sanapana with a relatively small number of speakers. expanding on amr, umr also includes a documentlevel representation that represents linguistic relations that go beyond sentence-boundaries, such as coreferential relations and temporal and modal dependencies. like amr, the basic building blocks of a umr graph are concepts and relations, with concepts typically mapping to words in a sentence and relations representing how those words are related semantically. umr concepts are typically lemmas or sense-disambiguated lemmas, but they can also be abstract concepts that do not map to specific word tokens and are instead inferred from the context of the sentence. common in a umr graph are subgraphs that represent predicate-argument structures in which the predicate is the parent and its arguments are its children. the relations between a predicate and its arguments are typically the semantic roles that each argument plays with respect to the predicate, but they can also be other types of semantic relations. a umr example containing a multiword expression (mwe) is provided in (1): the mapping between umr concepts and words in a sentence is complex. while most umr concepts map to single words, there are also umr concepts as in (1) that map to multiple words, in this case four words. the opposite is true as well where one word can map to multiple umr concepts, which is often the case in polysynthetic languages like arapaho @xcite . in this paper, we draw from a broad range of annotated examples from different languages to discuss the properties of different types of mwes and how they can be annotated in umr. because umr annotation occurs in two stages-one called stage 0 for languages without rolesets, and one called stage 1 for languages with rolesets-we discuss differences in the strategies used for mwe annotation at both stages. here we adopt an operational definition of mwe in the context of umr annotation. when multiple words map to a single concept in umr, these words form an mwe. since they are so common, formulating a consistent approach for representing mwes in umr is critical to the success of umr as a representation. this requires, first of all, that we have a good understanding of what types of mwes exist in languages of the world, and then design a consistent set of guidelines to direct their annotation in umr. the mwes that we consider in this paper include light verb constructions (lvcs) (section 2), mwes without a strong figurative interpretation (section 3), non-consecutive multiword expressions that occur in certain constructions (section 4), idioms (section 5), proverbs (section 6), and two-part allegorical sayings (section 6.1). these categories distinguish mwes by how they are handled in umr, both in terms of how tokens are incorporated into umr graphs, and whether/how figurative meaning is conveyed through the umr schema. we expect that this work will be useful for umr annotation in the future, and is broadly relevant to studies of abstract meaning in formal semantic representations. ## light verb constructions light verb constructions take the form of a semantically-light verb with a nominal predicate as its object. the arguments are selected by the nominal predicate rather than the light verb, although the light verb can contribute proto-roles as well as aspectual interpretations of the construction as a whole. following amr, the umr concept used to represent the lvc in a graph is derived from the nominal predicate, with the light verb contributing to the aspectual annotation for the predicate. in the english example in (2), the light verb "make" pairs with the nominal pred-icate "break", which has arguments for an entity in motion (literal or abstract) and a destination. in the umr graph, the light verb is glossed by the appropriate propbank sense break-20, along with its arguments foot_0 . lvcs are also common in chinese in (3), for example, the light verb 获得 ("get") takes a nominal predicate 认 可 ("acceptance") as its object and together they form an lvc in which the nominal predicate selects the arguments and the light verb contributes to a performance aspectual value, meaning the acceptance event has been successfully completed. with the spanish lvc "dar miedo" (scare, lit. give someone fear), umr annotation omits the light verb and substitutes the whole construction with the roleset for the verb "asustar," which also means to scare foot_1 . in czech, the same phenomenon exists foot_2 . example (5) shows the lvc "vznést připomínku" ("to comment" or "to remind," lit. "raise a reminder"), which is similar to the previous spanish example in that the lvc can be represented in the umr graph with a roleset for a synonymous verb "připomenout" (lit. "to remind") foot_3 . there are also cases in czech, however, where the lvc is complex and cannot be replaced by a single related verbal roleset without some of the meaning being lost. an example is "projít zkouškou ohněm" (lit. "go_through test [by]fire", experience ordeal by fire). here, the nominal portion is itself idiomatic. the preferred umr approach in such cases is to use an mwe predicate reflecting the whole construction. ## mwes without strong figurative interpretation some mwes do not have a strong figurative interpretation, i.e., any figurative interpretation can still be derived from the parts. this category includes everything from fully-fixed mwes like complex function words @xcite to semi-fixed mwes @xcite like verb particle constructions (vpcs) and 'decomposable' idioms @xcite , as well as everything in between. fixed mwes are consecutive and do not vary at all, while semi-fixed mwes can allow a wide array of variations. where lexical variation is allowed, it ranges from inflection to token addition, alternation, or elision. some semi-fixed mwes include a core set of tokens that provide the key semantics and are never altered beyond inflection. these can be combined with additional tokens that can be replaced or modified to contextualize the mwe's semantics. some semi-fixed mwes have a fixed word order, and others, such as many vpcs, allow variable word order. umr has a similar array of treatments for annotating these mwes. at any annotation stage, core tokens can be concatenated to form the concept used in the umr graph. if the mwe is clausal, requires sensedisambiguation, or has a distinct argument structure, a new roleset can be created for it. ## non-consecutive constructions another challenge in umr annotation lies in representing constructions that are cued by a non-consecutive (and sometimes inter-clausal) sequence of words. they are also mwes in the sense that they consist of multiple words, but the words may be predominantly function words, and the meaning may not be derived from any one particular word in the sequence. following amr, umr uses abstract rolesets to represent the established semantics of such constructions. for example, "the more ... the more ..." (the xer, the yer) is annotated with an abstract roleset called correlate-91, which take as arguments the two predicates that are correlated: chinese has a similar construction that also maps to the abstract concept correlate-91: :arg1 (x3 / 临近-01 . the same example could be given for czech, with the correlation expressed via a pair of pronouns "čím ..., tím ...", for example "čím důležitější schůzka, tím jsem nervóznější.", lit. "the more important the meeting [is], the more nervous i am." ## idioms -mwes that have a figurative interpretation idioms are mwes that are ambiguous between a literal meaning and a figurative interpretation, where ambiguity can be resolved in context. depending on how mwes are interpreted, they are mapped to umr concepts in different ways. if the literal meaning is intended given the context, such an expression can be represented compositionally in umr. however, if the figurative meaning is intended, umr concepts are created by concatenating the word tokens in the mwe, similar to how fixed or semi-fixed expressions are handled. we illustrate this with the expression "jump on the bandwagon" in english, with a graph for the literal interpretation in (15) and one for the figurative interpretation (16). with the idiomatic interpretation, an english prop-bank roleset jump-on-bandwagon-09 is used, shown in figure rolesets for idiomatic mwes are able to be quite descriptive about the relationships between the mwe's tokens and between the elements in the literal and figurative frames, as illustrated in figure (1 ). first, numbered roles are provided for participants in the idiomatic frame as well as any modifiers the expression can take. here, the arg0 of jump-on-bandwagon-09 corresponds to the same agent that appears in (15). additionally, the expression 'jump on the bandwagon' is modifiable, so the roleset provides an arg1 for any phrase that might be used to modify 'bandwagon'. the idiomatic meaning of the expression is 'to join in with others who are following a certain fad', and it conveys this by evoking historical imagery of political parade-goers jumping onto the wagon that carried the band at the front of the parade. in current use, speakers identify the 'bandwagon' in the expression with a fad, and tell us what the fad is by modifying that token syntactically. next in the roleset, the tokens are identified and labeled with slot position, part of speech, and syntactic head. then, two parallel graphs are given that use the slot labels (a-d), the numbered arguments (n-arg0 and n-arg1), and token values to map between the literal frame and the metaphorical frame. the token 'jump' in slot a is equated with the 'jump-03' roleset (physical jumping) in the literal interpretation and with the 'join-in-05' roleset (joining a group) in the idiomatic interpretation. the token 'bandwagon' in slot d appears as the destination argument of jump-03 in the literal frame and is equated with 'people following a fad' in the figurative frame. n-arg0 is equated with the literal jumper and the figurative fad-joiner. lastly, n-arg1 tells us what kind of fad is being discussed in the figurative frame (as in he joined the bitcoin boom). tokens: jump on the bandwagon slots: a b c d pos: vb pp det nn head: -a d b idiom mapping: literal: (a / jump-03 :arg0 (n / n-arg0) :destination (d / bandwagon)) figurative: (a / join-in-05 :arg0 (n / n-arg0) :arg1 (d / people :arg0-of (f / follow-01 :arg1( f2/ fad :topic (n1 / n-arg1)) jump-on-bandwagon-09 :arg0 fad-follower :arg1 the fad definition: join with others in following a fad jump-03 physically leap :arg0 jumper join-in-05 join a group :arg0 joiner :arg1 group follow-02 adhere to :arg0 follower :arg1 thing adhered to ## proverbs like idioms, proverbs also have a literal and figurative interpretation. unlike idioms, however, proverbs are often self-contained sentences with all participants of the predicates filled, and it is hard to construct alternative contexts in which the proverbs can be interpreted literally. since they tend to be longer than idioms and their literal meaning can be constructed compositionally in umr, we annotate proverbs with an abstract roleset called proverb-91, which takes two arguments. the first argument is required and is annotated compositionally; the second argument will be described in the next section. we illustrate a standard proverb that uses the arg1 with a chinese proverb in ( 24 ). ## related & future work mwes have always been a thorny issue for computational linguistics @xcite and have been studied from various perspectives, from linguistic modeling (annotation) to automatic identification. the existence of a series of workshops focusing on mwes @xcite @xcite attests to the interest of a large community of researchers. for many european languages, multiword expressions including verbal ones have been tackled in the parseme project foot_6 @xcite . for czech, previous work on identification and extraction of verbal (and other) mwes from treebanks is described in @xcite @xcite . this work addresses the representation of mwes in umr, resolving many of the issues surrounnding many-to-one word-to-concept mappings. it builds on previous work respecting light verb constructions in multilingual propbanks @xcite and amr annotation of certain english constructions @xcite , and expands it to include additional mwes in multiple languages. we have discussed different types of mwes that present challenges to umr annotation and presented solutions for their treatment. users with an existing valency lexicon or propbank may wish to undertake creating new mwe rolesets based on recommendations we have outlined. forthcoming work will build on the strategies outlined here as we tackle one-tomany word-to-concept mappings.