[PDF] Top 20 A Discriminative Graph Based Parser for the Abstract Meaning Representation

... into meaning representa- tions. Abstract Meaning Representation (AMR) (Banarescu et ...the meaning of a sentence is encoded as a rooted, directed, acyclic ...AMR graph. The ... See full document

... Validity Graph-based parsers like Dozat and Man- ning (2018); Zhang et ...builds meaning representations: at each decoding step, it takes a semantic relation as input, and has memory of preceding ... See full document

... An example sentence, logical triples and corre- sponding AMR graph is illustrated in Figure 1 (left part). AMR introduces variables (graph nodes) for entities, events, properties, and states. Each node in ... See full document

... Abstract In this paper we describe the data that will be used to compare the semantic struc- tures that emerge from synonymy in French and in Mandarin. We aim at studying these semantic structures at both a ... See full document

... Our parser tends to predict a smaller graph, so for some frameworks which tend to have bigger graphs, such as PSD and EDS, the MRP results of our parser are ... See full document

... explored based on the performance on the development ...parse graph, and the corresponding feature values, for trigger candidate “acquisition”, from the above example AMR ... See full document

... AMR parser, we examine what would happen if we had an NER model that was perfectly accurate for a given set of concept ...the parser in exper- iment 2, and in experiment 3 we use all concepts that are not ... See full document

... selected abstract meaning representation (AMR) as syntac- tic and semantic information, and proposed an attention-based AMR encoder-decoder ...automatic parser can help to improve the ... See full document

... a graph- ical semantic meaning representation that predates AMR, but is intimately ...tree parser adapted to graphs and with additional constraints for their re- lation identifications (SRL++) ... See full document

... AMR parsing for languages other than English has made only a few steps forward. In previous work (Li et al., 2016; Xue et al., 2014; Bojar, 2014), nodes of the target graph were labeled with ei- ther English words ... See full document

... Non-feature based approaches like graph kernels compare syntactic structures directly (Airola et ...Rule based methods that either use manually crafted rules or generate rules from training data ... See full document

... We therefore follow syntax-based SMT custom and use string/string alignment models in align- ing our graph/string pairs. However, while it is straightforward to convert syntax trees into strings data (by ... See full document

... Having created a gold standard sample of our data, we ran both JAMR 4 (Flanigan et al., 2014) and CAMR 5 (Wang et al., 2015) on the same sam- ple and obtained the Smatch scores when com- pared to the gold standard. We ... See full document

... The second is AMREager (Damonte et al., 2017), which is a transition-based parser that works by scanning the string left-to-right and building the graph as the scan proceeds. This ... See full document

... In this section we discuss various transformations to our AMR data. Initially, we concern ourselves with converting AMR into a form that is amenable to GHKM rule extraction and string to tree decod- ing. We then turn to ... See full document

... the parser incremen- tality. We now show that our greedy transition- based AMR parser is linear-time in n, the length of the input sentence ...output graph has size ...the graph by ... See full document

... best parser for SDP is graph-based (Dozat and Manning, 2018), which assumes dependency graphs but cannot be directly applied to UCCA, EDS, and AMR, due the existence of concept ...EDS parser, ... See full document

... To our knowledge, our system is the first for gen- erating English from AMR. The approach is a sta- tistical natural language generation (NLG) system, trained discriminatively using sentences in the AMR bank (Banarescu ... See full document

... We extract substructures (subtrees) correspond- ing to NPs from the AMR Bank (LDC2014T12). In the AMR Bank, there is no alignment be- tween the words and the concepts (nodes) in the AMR graphs. We obtain this alignment ... See full document

... Cross-lingual Abstract Meaning Representation parsing (Damonte and Cohen, 2017) for which we do not require a standard gold data seems to overcome the structural differences between English and a ... See full document