An annotated corpus for the analysis of VP ellipsis

Verb Phrase Ellipsis (VPE) has been studied in great depth in theoretical linguistics, but empirical studies of VPE are rare. We extend the few previous corpus studies with an annotated corpus of VPE in all 25 sections of the Wall Street Journal corpus (WSJ) distributed with the Penn Treebank. We annotated the raw files using a stand-off annotation scheme that codes the auxiliary verb triggering the elided verb phrase, the start and end of the antecedent, the syntactic type of antecedent (VP, TV, NP, PP or AP), and the type of syntactic pattern between the source and target clauses of the VPE and its antecedent. We found 487 instances of VPE (including predicative ellipsis, antecedent-contained deletion, comparative constructions, and pseudo-gapping) plus 67 cases of related phenomena such as do so anaphora. Inter-annotator agreement was high, with a 0.97 average F-score for three annotators for one section of the WSJ. Our annotation is theory neutral, and has better coverage than earlier efforts that relied on automatic methods, e.g. simply searching the parsed version of the Penn Treebank for empty VP's achieves a high precision (0.95) but low recall (0.58) when compared with our manual annotation. The distribution of VPE source-target patterns deviates highly from the standard examples found in the theoretical linguistics literature on VPE, once more underlining the value of corpus studies. The resulting corpus will be useful for studying VPE phenomena as well as for evaluating natural language processing systems equipped with ellipsis resolution algorithms, and we propose evaluation measures for VPE detection and VPE antecedent selection. The stand-off annotation is freely available for research purposes

Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.Comment: EMNLP 201

A corpus-based analysis of Post-Auxiliary Ellipsis voice mismatches in Late Modern English

This paper analyses Post-Auxiliary Ellipsis voice mismatches between the antecedent clause(s) and the ellipsis site(s) in Late Modern English, using the Penn Parsed Corpus of Modern British English (PPCMBE) (1700-1914). This study focuses on two subtypes of Post-Auxiliary Ellipsis, namely VP ellipsis and Pseudogapping. The results show that voice mismatches were possible in Pseudogapping and VP ellipsis in Late Modern English with low frequencies. This fact serves as counterevidence for the claim about the impossibility of finding voice mismatches in Pseudogapping and confirms corpus-based findings for Present-Day English. As for VP ellipsis, corpus-based studies show that voice mismatches are not attested in Present-Day English. Since they occur in Late Modern English with low frequencies, this contrast may be due to the stylistics or register of the corpora analysed.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under [Grant numbers FFI2013-44065-P and FPI BES-2010-030869]; the Autonomous Government of Galicia under Grant number [GPC2014/060]; and the Labex Mobility Grant

ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference

In this paper, we describe ParCorFull2.0, a parallel corpus annotated with full coreference chains for multiple languages, which is an extension of the existing corpus ParCorFull (Lapshinova-Koltunski et al., 2018). Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation (MT) and other multilingual natural language processing (NLP) applications. The current version of the corpus that we present here contains not only parallel texts for the language pair English-German, but also for English-French and English-Portuguese, which are all major European languages. The new language pairs belong to the Romance languages. The addition of a new language group creates a need of extension not only in terms of texts added, but also in terms of the annotation guidelines. Both French and Portuguese contain structures not found in English and German. Moreover, Portuguese is a pro-drop language bringing even more systemic differences in the realisation of coreference into our cross-lingual resources. These differences cause problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies.Christian Hardmeier and Elina Lartaud were supported by the Swedish Research Council under grant 2017-930, which also funded the annotation work of the French data. Pedro Augusto Ferreira was supported by FCT, Foundation for Science and Technology, Portugal, under grant SFRH/BD/146578/2019

Ellipsis Resolution as Question Answering: An Evaluation

Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).Comment: To appear in EACL 202

C-structures and f-structures for the British national corpus

We describe how the British National Corpus (BNC), a one hundred million word balanced corpus of British English, was parsed into Lexical Functional Grammar (LFG) c-structures and f-structures, using a treebank-based parsing architecture. The parsing architecture uses a state-of-the-art statistical parser and reranker trained on the Penn Treebank to produce context-free phrase structure trees, and an annotation algorithm to automatically annotate these trees into LFG f-structures. We describe the pre-processing steps which were taken to accommodate the differences between the Penn Treebank and the BNC. Some of the issues encountered in applying the parsing architecture on such a large scale are discussed. The process of annotating a gold standard set of 1,000 parse trees is described. We present evaluation results obtained by evaluating the c-structures produced by the statistical parser against the c-structure gold standard. We also present the results obtained by evaluating the f-structures produced by the annotation algorithm against an automatically constructed f-structure gold standard. The c-structures achieve an f-score of 83.7% and the f-structures an f-score of 91.2%