Comprehensive Supersense Disambiguation of English Prepositions and Possessives

Abstract

Semantic relations are often signaled with prepositional or possessive marking-but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker's lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.

Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses

“…tems which predict a terminal-level SNACS label (before it is mapped to a higher relation in postprocessing), we compare SNACS disambiguation performance against (Schneider et al, 2018) in §5.3.…”

“…(Others, such as QUANTITY and WHOLE, are for entity modification.) But unlike VerbNet, FrameNet (Fillmore and Baker, 2009), and PropBank (Palmer et al, 2005), SNACS does not require a language-specific predicate lexicon (hence Schneider et al (2018) use the term "supersenses", which we adopt in the remainder of this paper)-and is therefore compatible with UCCA's design principle of crosslinguistic applicability. 5 Currently, SNACS labels are applied directly to lexical items, without marking up underlying structure on either the subword (morphological) or the sentence-structure level.…”

“…9 This is added in preprocessing in the same way as the syntactic features listed above (including the BiLSTM encoding). At testing time, we obtain SNACS labels for automatically identified targets from the SVM model of Schneider et al (2018).…”

“…Here we train the SNACS task in direct interaction with the UCCA parsing task. 10 Note that our setup differs from that of Hershcovich et al (2018) in two key points: In contrast to the auxiliary tasks used in the aforementioned work, SNACS prediction as formulated by Schneider et al (2018) is not a structured, but a (per-token) classification task (however, as described above, we transform it into an artificially structured task to make it conform with the input format expected by TUPA). Furthermore, we are interested in both UCCA and SNACS performance, expecting both tasks to benefit from each other's complementary semantic content.…”

“…On the one hand, Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) provides I went to ohm after reading some of the reviews a skeletal structure of semantic units and relations, with typologically-based criteria for marking predicate-argument structures, based on "Basic Linguistic Theory", an established framework for typological description (Dixon, 2010(Dixon, /2012). On the other hand, a recent approach to annotation of English prepositions and possessives (SNACS; Schneider et al, 2018) provides an inventory of labels that characterize semantic relations. UCCA and SNACS follow similar design principles: they are both language-neutral, with general-purpose coarse-grained labels rather than lexically-specific senses or roles; and they are both designed for direct semantic annotation, without requiring a syntactic parse as a foundation.…”