Index of papers in Proc. ACL 2014 that mention
  • parsing algorithm
Feng, Vanessa Wei and Hirst, Graeme
Bottom-up tree-building
Therefore, our model incorporates the strengths of both HILDA and Joty et al.’s model, i.e., the efficiency of a greedy parsing algorithm , and the ability to incorporate sequential information with CRFs.
Introduction
However, as an optimal discourse parser, Joty et al.’s model is highly inefficient in practice, with respect to both their DCRF-based local classifiers, and their CKY-like bottom-up parsing algorithm .
Introduction
CKY parsing is a bottom-up parsing algorithm which searches all possible parsing paths by dynamic programming.
Introduction
As a result of successfully avoiding the expensive non-greedy parsing algorithms , our discourse parser is very efficient in practice.
Related work
Their model assigns a probability to each possible constituent, and a CKY-like parsing algorithm finds the globally optimal discourse tree, given the computed probabilities.
Related work
The inefficiency lies in both their DCRF—based joint model, on which inference is usually slow, and their CKY-like parsing algorithm , whose issue is more prominent.
Results and Discussion
gSVMFH in the second row is our implementation of HILDA’s greedy parsing algorithm using Feng and Hirst (2012)’s enhanced feature set.
Results and Discussion
First, as shown in Table 2, the average number of sentences in a document is 26.11, which is already too large for optimal parsing models, e.g., the CKY—like parsing algorithm in jCRF, let alone the fact that the largest document contains several hundred of EDUs and sentences.
parsing algorithm is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Bengoetxea, Kepa and Agirre, Eneko and Nivre, Joakim and Zhang, Yue and Gojenola, Koldo
Experimental Framework
Table 1: LAS results with several parsing algorithms , Penn2Ma1t conversion (T: p <0.05, 1;: p <0.005).
Experimental Framework
Table 2: LAS results with several parsing algorithms in the LTH conversion (T: p <0.05, 1;: p <0.005).
Results
We can also conclude that automatically acquired clusters are specially effective with the MST parser in both treebank conversions, which suggests that the type of semantic information has a direct relation to the parsing algorithm .
parsing algorithm is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Parikh, Ankur P. and Cohen, Shay B. and Xing, Eric P.
Abstract
Although finding the “minimal” latent tree is NP-hard in general, for the case of projective trees we find that it can be found using bilexical parsing algorithms .
Abstract
While this criterion is in general NP-hard (Desper and Gascuel, 2005), for projective trees we find that a bilexical parsing algorithm can be used to find an exact solution efficiently (Eisner and Satta, 1999).
Abstract
However, if we restrict u to be in L1, as we do in the above, then maximizing over Ll can be solved using the bilexical parsing algorithm from Eisner and Satta (1999).
parsing algorithm is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: