[PDF] Top 20 Multiplicative Representations for Unsupervised Semantic Role Induction

... new unsupervised semantic role la- beling approach that learns embeddings of argu- ments by predicting each argument from its con- text and considering dependency relation as a mul- tiplicative ...on ... See full document

... Input Corpus. In our evaluation, we use triple frequencies from the DepCC dataset (Panchenko et al., 2018) , which is a dependency-parsed ver- sion of the Common Crawl corpus, and the stan- dard 300-dimensional word ... See full document

... using unsupervised approaches to induce the frames evoked by a collection of docu- ...embedding representations of the verbs and ar- guments. Using such representations is appro- priate in the ... See full document

... ple representations per word have been proposed in the literature, based on clustering local con- texts of individual words (Reisinger and Mooney, 2010; Tian et ...utilize semantic lexical resources (Rothe ... See full document

... Two versions of the proposed model are reported in the last rows of Table 3: one with random (uni- form) initialization of all variables, and the other with deterministic initialization of all R i from the syntactic ... See full document

... Since linkings are injective, i.e., no two seman- tic roles are mapped onto the same syntactic func- tion, the canonical function of an argument uniquely references a specific semantic role. We define a ... See full document

... the role assigned to this argument (i.e. Patient) and other role-argument pairs ((Agent, police) and (Instrument, ...for role assignments which simplify this prediction task as much as ...these ... See full document

... Recent work on SRL has explored approaches that can leverage unlabelled data, following a semi-supervised (F¨urstenau and Lapata, 2012; Titov and Klementiev, 2012) or unsupervised learning paradigm (Abend et al., ... See full document

... multiple representations, we define the distance from any element of E to the source as the average distance over the spaces and use the decreasing distances as the order in which to test whether a node is a ... See full document

... We explore the extent to which high- resource manual annotations such as tree- banks are necessary for the task of se- mantic role labeling (SRL). We examine how performance changes without syntac- tic ... See full document

... on unsupervised SRL where separate models were induced for each predicate (Grenager and Manning, 2006; Lang and Lapata, 2010, 2011a,b; Garg and Henderson, 2012; Fürstenau and Rambow, ...potential semantic ... See full document

... the role induction problem has been studied in Lang and Lapata (2010) where it has been reformulated as a problem of detect- ing alterations and mapping non-standard link- ings to the canonical ...the ... See full document

... shallow semantic parsing task has also been considered in the work of Poon and Domingos (2009; 2010), but using a MLN model and, again, only on the biomedical ... See full document

... In this work we propose to learn these features and their complex interactions (e.g., selectional restrictions) automatically from data. Specifi- cally, we induce embeddings to represent a pred- icate and its arguments. ... See full document

... For calculating purity, each induced cluster (or role) is mapped to a particular gold role that has the maximum instances in the cluster. Analyzing the output of our model (line 1c in Table 1), we found ... See full document

... vised semantic role induction which we for- malize as a graph partitioning ...their role-semantic ...other unsupervised approaches in terms of F1 whilst attaining significantly ... See full document

... Comparison Models We compared our split- merge algorithm against two competitive ap- proaches. The first one assigns argument instances to clusters according to their syntactic function (e.g., subject, object) as ... See full document

... The MLN above has one problem: it often clusters expressions that are semantically oppo- site. For example, it clusters antonyms like “el- derly/young”, “mature/immature”. This issue also occurs in other ... See full document

... mantic role induction) could be solved much more effectively using a classifier than any kind of clus- tering because generic roles look more like a high- level linguistic abstraction than something natu- ... See full document

... each semantic frame in a sen- tence explains puzzling results in recent work on the MEANT family of semantic MT eval- uation metrics, which have disturbingly in- dicated that dissociating semantic ... See full document