In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification : quantification, negation (generally), and modality. The resulting representation, which we call Uniform Meaning Representation (UMR), adopts the predicative core of AMR and embeds it under a scope graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways : (a) they are more transparent ; and (b) they specify default scope when possible. ‘

Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved. We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs. The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English. We demonstrate these inferences on four classes of conversationally-oriented inferences in a mixed genre dataset with 68.5 % precision from human judgments.

The view that the representation of information structure (IS) should be a part of (any type of) representation of meaning is based on the fact that IS is a semantically relevant phenomenon. In the contribution, three arguments supporting this view are briefly summarized, namely, the relation of IS to the interpretation of negation and presupposition, the relevance of IS to the understanding of discourse connectivity and for the establishment and interpretation of coreference relations. Afterwards, possible integration of the description of the main ingredient of IS into a meaning representation is illustrated.

It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective‘s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.

Ellipsis is very common in language. It’s necessary for natural language processing to restore the elided elements in a sentence. However, there’s only a few corpora annotating the ellipsis, which draws back the automatic detection and recovery of the ellipsis. This paper introduces the annotation of ellipsis in Chinese sentences, using a novel graph-based representation Abstract Meaning Representation (AMR), which has a good mechanism to restore the elided elements manually. We annotate 5,000 sentences selected from Chinese TreeBank (CTB). We find that 54.98 % of sentences have ellipses. 92 % of the ellipses are restored by copying the antecedents’ concepts. and 12.9 % of them are the new added concepts. In addition, we find that the elided element is a word or phrase in most cases, but sometimes only the head of a phrase or parts of a phrase, which is rather hard for the automatic recovery of ellipsis.

Humans have the unique ability to infer information about participants in a scene, even if they are not mentioned in a text about that scene. Computer systems can not do so without explicit information about those participants. This paper addresses the linguistic phenomenon of null-instantiated frame elements, i.e., implicit semantic roles, and their representation in FrameNet (FN). It motivates FN’s annotation practice, and illustrates three types of null-instantiated arguments that FrameNet tracks, noting that other lexical resources do not record such semantic-pragmatic information, despite its need in natural language understanding (NLU), and the elaborate efforts to create new datasets. It challenges the community to appeal to FN data to develop more sophisticated techniques for recognizing implicit semantic roles, and creating needed datasets. Although the annotation of null-instantiated roles was lexicographically motivated, FN provides useful information for text processing, and therefore must be considered in the design of any meaning representation for natural language understanding.

This paper concerns the application of Abstract Meaning Representation (AMR) to Korean. In this regard, it focuses on the copula construction and its negation and the case-stacking phenomenon thereof. To illustrate this clearly, we reviewed the : domain annotation scheme from various perspectives. In this process, the existing annotation guidelines were improved to devise annotation schemes for each issue under the principle of pursuing consistency and efficiency of annotation without distorting the characteristics of Korean.

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participantsincluding subjects and objectsdirectly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.

This paper presents a case study on meaning representation for Vietnamese. Having introduced several existing semantic representation schemes for different languages, we select as basis for our work on Vietnamese AMR (Abstract Meaning Representation). From it, we define a meaning representation label set by adapting the English schema and taking into account the specific characteristics of Vietnamese.

This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.

Natural language understanding at the semantic level and independent of language variations is of great practical value. Existing approaches such as semantic role labeling (SRL) and abstract meaning representation (AMR) still have features related to the peculiarities of the particular language. In this work we describe various challenges and possible solutions in designing a semantic representation that is universal across a variety of languages.

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.