[PDF] Top 20 A Robust Combination Strategy for Semantic Role Labeling
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A Robust Combination Strategy for Semantic Role Labeling
... For robustness, the inference model uses only global attributes extracted from the solutions pro- vided by the individual systems, e.g., the sequence of role labels generated by each system for the cur- rent ... See full document
8
Focusing Annotation for Semantic Role Labeling
... Annotation of data is a time-consuming process, but nec- essary for supervised machine learning approaches. Most state-of-the-art solutions to NLP tasks, including seman- tic role labeling (SRL), are driven ... See full document
5
Tree Kernels for Semantic Role Labeling
... These points suggest that tree kernels should always be applied, at least for an initial study of the problem. Unfortunately, they suffer from two main limitations: (a) poor impact on boundary detection as, in this task, ... See full document
32
Syntax for Semantic Role Labeling, To Be, Or Not To Be
... During the pruning of argument candidates, we use the officially predicted syntactic parses pro- vided by CoNLL-2009 shared-task organizers on both English and Chinese. Figure 3 shows chang- ing curves of coverage and ... See full document
11
Towards Open Domain Semantic Role Labeling
... different strategy to incorporate lexical features into clas- sification models is ...pruning strategy is shown to achieve high accuracy over a chunk- ing and named entity recognition ...effective ... See full document
10
A Dual Layer Semantic Role Labeling System
... groups: semantic role labeling and con- cept extraction. Semantic role labeling (SRL) has sparked much interest in NLP (Shen and Lapata, 2007; Liu and Gildea, ... See full document
6
Multilingual Semantic Role Labeling
... robust and can handle incorrect syntactic parse trees with a good level of immunity. While input parse trees in Chinese and German had a labeled syntac- tic accuracy of 78.46 (Hajiˇc et al., 2009), we could reach ... See full document
6
Exploring Multilingual Semantic Role Labeling
... Three different algorithms were tried during the development period: support vector machines (SVM), distance-weighted k-Nearest Neighbor (kNN) (Li et al., 2004), and Naïve Bayes with mul- tinomial model (Mccallum and ... See full document
6
A Joint Model for Extended Semantic Role Labeling
... SRL combination system of Surdeanu et al. (2007) studied the combination of three different SRL systems using constraints and also by training secondary scoring functions over the individual sys- ...SRL ... See full document
11
Semantic Role Labeling for Open Information Extraction
... Open Information Extraction is a recent paradigm for machine reading from arbitrary text. In contrast to existing techniques, which have used only shallow syntactic features, we investigate the use of semantic ... See full document
9
Semantic Role Labeling of Emotions in Tweets
... of semantic role labeling of emotions in tweets described earlier in the paper, we treat the detection of emotional state and stimulus as two subtasks for which we train state-of-the-art support ... See full document
10
Towards Robust Semantic Role Labeling
... Table 2 shows the performance for training and testing on WSJ, and for training on WSJ and testing on Brown. There is a significant reduction in per- formance when the system trained on WSJ is used to label data from the ... See full document
8
Towards Robust Semantic Role Labeling
... with semantic structure can play a key role in NLP applications such as information extraction (Harabagiu, Bejan, and Morarescu 2005), question answering (Narayanan and Harabagiu 2004), and ... See full document
22
A Minimum Error Weighting Combination Strategy for Chinese Semantic Role Labeling
... that combination is a robust and effective method to alleviate SRL’s dependency on pars- ing results (M`arquez et ...of combination for Chinese SRL task is still ...existing combination ... See full document
9
Semantic Role Labeling Improves Incremental Parsing
... feature combination we try also includes the following baseline features: Prefix Tree Probability is the log probability of the prefix tree as scored by the probability model of the baseline ... See full document
11
Multi Predicate Semantic Role Labeling
... Role labeling of the shared arguments is anoth- er key point. The predicates and their shared argu- ment could be considered as a joint structure, with strong dependencies between the shared argumen- t’s ... See full document
11
A Sequence to Sequence Model for Semantic Role Labeling
... Collobert et al. (2011) proposed the first SRL neural model that did not depend on hand-crafted features and treated the task as an IOB sequence labeling problem. Later, Zhou and Xu (2015) pro- posed a deep ... See full document
10
Adapting Self Training for Semantic Role Labeling
... One important supportive factor of studying supervised statistical SRL has been the existence of hand-annotated semantic corpora for training SRL systems. FrameNet (Baker et al., 1998) was the first such resource, ... See full document
6
Starting from Scratch in Semantic Role Labeling
... verbs is much more troublesome. Verbs’ mean- ings are abstract, therefore harder to identify based on scene information alone (Gillette et al., 1999). As a result, early vocabularies are dominated by nouns (Gentner, ... See full document
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