Abstract | We explore the extent to which high-resource manual annotations such as treebanks are necessary for the task of semantic role labeling (SRL). |
Approaches | A typical pipeline consists of a POS tagger, dependency parser, and semantic role labeler . |
Approaches | Dependency-based semantic role labeling can be described as a simple structured prediction problem: the predicted structure is a labeled directed graph, where nodes correspond to words in the sentence. |
Approaches | Semantic Dependency Model As described above, semantic role labeling can be cast as a structured prediction problem where the structure is a labeled semantic dependency graph. |
Discussion and Future Work | We have compared various approaches for low-resource semantic role labeling at the state-of-the-art level. |
Experiments | To compare to prior work (i.e., submissions to the CoNLL-2009 Shared Task), we also consider the joint task of semantic role labeling and predicate sense disambiguation. |
Introduction | The goal of semantic role labeling (SRL) is to identify predicates and arguments and label their semantic contribution in a sentence. |
Related Work | Our work builds upon research in both semantic role labeling and unsupervised grammar induction (Klein and Manning, 2004; Spitkovsky et a1., 2010a). |
Related Work | Previous related approaches to semantic role labeling include joint classification of semantic arguments (Toutanova et a1., 2005; J 0-hansson and Nugues, 2008), latent syntax induction (Boxwell et a1., 2011; Naradowsky et a1., 2012), and feature engineering for SRL (Zhao et a1., 2009; Bjorkelund et a1., 2009). |
Related Work | (2013) extend this idea by coupling predictions of a dependency parser with predictions from a semantic role labeler . |
Abstract | Additionally, we report strong results on PropBank-style semantic role labeling in comparison to prior work. |
Conclusion | Finally, we presented results on PropBank-style semantic role labeling with a system that included the task of automatic verb frame identification, in tune with the FrameNet literature; we believe that such a system produces more interpretable output, both from the perspective of human understanding as well as downstream applications, than pipelines that are oblivious to the verb frame, only focusing on argument analysis. |
Introduction | Most work on frame-semantic parsing has usually divided the task into two major subtasks: frame identification, namely the disambiguation of a given predicate to a frame, and argument identification (or semantic role labeling ), the analysis of words and phrases in the sentential context that satisfy the frame’s semantic roles (Das et al., 2010; Das et al., 2014).1 Here, we focus on the first subtask of frame identification for given predicates; we use our novel method (§3) in conjunction with a standard argument identification model (§4) to perform full frame-semantic parsing. |
Introduction | Second, we present results on PropBank-style semantic role labeling (Palmer et al., 2005; Meyers et al., 2004; Marquez et al., 2008), that approach strong baselines, and are on par with prior state of the art (Punyakanok et al., 2008). |
Overview | 2004; Carreras and Marquez, 2005) on PropBank semantic role labeling (SRL), it has been treated as an important NLP problem. |
Overview | PropBank The PropBank project (Palmer et al., 2005) is another popular resource related to semantic role labeling . |
Overview | Generic core role labels (of which there are seven, namely A0-A5 and AA) for the verb frames are marked in the figure.3 A key difference between the two annotation systems is that PropBank uses a local frame inventory, where frames are predicate-specific. |
Assumptions | Finally, following the finding by Gertner and Fisher (2012) that children interpret intransitives with conjoined subjects as transitives, this work assumes that semantic roles have a one-to-one correspondence with nouns in a sentence (similarly used as a soft constraint in the semantic role labelling work of Titov and Klementiev, 2012). |
Background | to acquire semantic role labelling while still exhibiting 1-1 role bias. |
Comparison to BabySRL | The acquisition of semantic role labelling (SRL) by the BabySRL model (Connor et al., 2008; Connor et al., 2009; Connor et al., 2010) bears many similarities to the current work and is, to our knowledge, the only comparable line of inquiry to the current one. |
Comparison to BabySRL | The primary function of BabySRL is to model the acquisition of semantic role labelling While making an idiosyncratic error Which infants also make (Gertner and Fisher, 2012), the 1-1 role bias error (John and Mary gorped interpreted as J ohn go'r’ped M a'r’y). |
Comparison to BabySRL | (2008) demonstrate that a supervised perceptron classifier, based on positional features and trained on the silver role label annotations of the BabySRL corpus, manifests 1-1 role bias errors. |
Discussion | Training significantly improves role labelling in the case of object-extractions, which improves the overall accuracy of the model. |
Evaluation | These annotations were obtained by automatically semantic role labelling portions of CHILDES with the system of Punyakanok et al. |
Related Work | MEANT (Lo et al., 2012), which is the weighted f-score over the matched semantic role labels of the automatically aligned semantic frames and role fillers, that outperforms BLEU, NIST, METEOR, WER, CDER and TER in correlation with human adequacy judgments. |
Related Work | There is a total of 12 weights for the set of semantic role labels in MEANT as defined in Lo and Wu (2011b). |
Related Work | For UMEANT (Lo and Wu, 2012), they are estimated in an unsupervised manner using relative frequency of each semantic role label in the references and thus UMEANT is useful when human judgments on adequacy of the development set are unavailable. |
XMEANT: a cross-lingual MEANT | The weights can also be estimated in unsupervised fashion using the relative frequency of each semantic role label in the foreign input, as in UMEANT. |
Abstract | In the described system, semantic role labels of source sentences are used in a domain-independent manner to generate both questions and answers related to the source sentence. |
Approach | SENNA provides the tokenizing, pos tagging, syntactic constituency parsing and semantic role labeling used in the system. |
Approach | SENNA produces separate semantic role labels for each predicate in the sentence. |
Linguistic Challenges | For example in: Plant roots and bacterial decay use carbon dioxide in the process of respiration, the word use was classified as NN, leaving no predicate and no semantic role labels in this sentence. |
Related Work | (2013), which used semantic role labeling to identify patterns in the source text from which questions can be generated. |
Class Analyses | Since dictionary publishers have not previously devoted much effort in analyzing preposition behavior, we believe PDEP may serve an important role, particularly for various NLP applications in which semantic role labeling is important. |
Class Analyses | We expect that desired improvements will come from usage in various NLP tasks, particularly word-sense disambiguation and semantic role labeling . |
Introduction | (2013); Srikumar and Roth (2011)) have shown the value of prepositional phrases in joint modeling with verbs for semantic role labeling . |
See http://clg.wlv.ac.uk/proiects/DVC | The occurrence of these invalid instances provides an opportunity for improving taggers, parsers, and semantic role labelers . |
Experiments | We then pass the parses to a Chinese semantic role labeler (Li et al., 2010), trained on the Chinese PropBank 3.0 (Xue and Palmer, 2009), to annotate semantic roles for all verbal predicates (part-of-speech tag VV, VE, or VC). |
Experiments | tactic parsing and semantic role labeling on the Chinese sentences, then train the models by using MaxEnt toolkit with L1 regularizer (Tsuruoka et al., 2009).3 Table 3 shows the reordering type distribution over the training data. |
Related Work | Finally in the postprocessing approach category, Wu and Fung (2009) performed semantic role labeling on translation output and reordered arguments to maximize the cross-lingual match of the semantic frames between the source sentence and the target translation. |