[PDF] Top 20 Efficient Inference and Structured Learning for Semantic Role Labeling
Has 10000 "Efficient Inference and Structured Learning for Semantic Role Labeling" found on our website. Below are the top 20 most common "Efficient Inference and Structured Learning for Semantic Role Labeling".
Efficient Inference and Structured Learning for Semantic Role Labeling
... using structured learning improves re- call at a slight expense of precision when compared to local ...the structured model can rely on the constraints to eliminate some ... See full document
14
Semantic Role Labeling for News Tweets
... As for SRL on news, most researchers used the pipelined approach, i.e., dividing the task into several phases such as argument identifica- tion, argument classification, global inference, etc., and conquering them ... See full document
9
Semantic Role Labeling via Tree Kernel Joint Inference
... Finally, the high computation time of the re- ranker prevented us to use the larger structures which include all arguments. The major complexity issue was the slow training and classification time of SVMs. The time ... See full document
8
Efficient Logical Inference for Semantic Processing
... the role that logical infer- ence could play in RTE task, and the efficiency of performing inference on abstract ...logical inference contributes at places that are somehow inconspicuous, there is ... See full document
5
Multi Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
... Most Semantic Role Labeling (SRL) ap- proaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires lin- guistic ...Active Learning frame- ... See full document
8
A Joint Model for Extended Semantic Role Labeling
... joint inference model that captures the inter-dependencies between verb seman- tic role labeling and relations expressed us- ing ...for learning a joint ... See full document
11
Semantic Role Labeling Via Integer Linear Programming Inference
... The learning algorithm used is a variation of the Winnow update rule incorporated in SNoW (Roth, 1998; Roth and Yih, 2002), a multi-class classifier that is specifically tailored for large scale learning ... See full document
7
A Study of Imitation Learning Methods for Semantic Role Labeling
... To see if increased variance is responsible for po- tential differences in the easyfirst variants, we con- struct a parallel situation with random orderings: rand-static and rand-dynamic. The first chooses a random ... See full document
10
Affordance Extraction and Inference based on Semantic Role Labeling
... While this work shares some of these same el- ements (i.e. SRL and word embeddings), they are used to predict potential affordances instead of se- lectional preferences. Consequently, our represen- tations are designed ... See full document
6
Towards Semi Supervised Learning for Deep Semantic Role Labeling
... on Semantic Role Label- ing ...of semantic-role corpora and are thus not well suited for low- resource languages or ...semi-supervised semantic role la- beling method that ... See full document
6
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
... of semantic role labeling in more ...and inference. The features used for building the classifiers and the learning algorithm applied are also explained ...of inference in a ... See full document
32
Enhancing Opinion Role Labeling with Semantic Aware Word Representations from Semantic Role Labeling
... Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion ...with semantic ... 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 a ... See full document
6
Grounded Semantic Role Labeling
... grounded semantic role labeling. Besides semantic roles ex- plicitly mentioned in language descriptions, our ap- proach also grounds implicit roles which are not explicitly ...mantic ... See full document
11
Polyglot Semantic Role Labeling
... We evaluate our system on the semantic role label- ing portion of the CoNLL-2009 shared task (Hajiˇc et al., 2009), on all seven languages, namely Cata- lan, Chinese, Czech, English, German, Japanese and ... See full document
6
Syntax for Semantic Role Labeling, To Be, Or Not To Be
... Our system performance is measured with the of- ficial script from CoNLL-2009 benchmarks, com- bining the output of our predicate disambigua- tion with our semantic role labeling. Our predi- cate ... See full document
11
Collective Semantic Role Labeling on Open News Corpus by Leveraging Redundancy
... Semantic Role Labeling (SRL, Màrquez, 2009) is generally understood as the task of identifying the arguments of a given predicate and assigning them semantic labels describing the roles they ... See full document
5
Low Resource Semantic Role Labeling
... We are interested in the effects of varied super- vision using pipeline and joint training for SRL. To compare to prior work (i.e., submissions to the CoNLL-2009 Shared Task), we also consider the joint task of ... See full document
11
Tree Kernels for Semantic Role Labeling
... high labeling accuracy, joint inference should be applied on the whole predicate–argument ...or semantic dependencies (Toutanova, Markova, and Manning ... See full document
32
Semantic Role Labeling Without Treebanks?
... Semantic role labeling is the process of generat- ing sets of semantic roles from syntactic analy- ...a semantic role la- beler, however, is costly in ...gold-standard ... See full document
9
Related subjects