Index of papers in Proc. ACL 2009 that mention
  • segmentations
Mochihashi, Daichi and Yamada, Takeshi and Ueda, Naonori
Experiments
Japanese word segmentation, with all supervised segmentations removed in advance.
Experiments
Semi-supervised results used only 10K sentences (1/5) of supervised segmentations .
Experiments
segmentations .
Inference
When we repeat this process, it is expected to mix rapidly because it implicitly considers all possible segmentations of the given string at the same time.
Inference
Segmentations before the final k characters are marginalized using the following recursive relationship:
Inference
Figure 4: Forward filtering of a[t] to marginalize out possible segmentations j before 75— k.
Introduction
In order to extract “words” from text streams, unsupervised word segmentation is an important research area because the criteria for creating supervised training data could be arbitrary, and will be suboptimal for applications that rely on segmentations .
Introduction
It is particularly difficult to create “correct” training data for speech transcripts, colloquial texts, and classics where segmentations are often ambiguous, let alone is impossible for unknown languages whose properties computational linguists might seek to uncover.
segmentations is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Kruengkrai, Canasai and Uchimoto, Kiyotaka and Kazama, Jun'ichi and Wang, Yiou and Torisawa, Kentaro and Isahara, Hitoshi
Experiments
We evaluated both word segmentation (Seg) and joint word segmentation and POS tagging ( Seg & Tag).
Experiments
For Seg , a token is considered to be a correct one if the word boundary is correctly identified.
Experiments
For Seg & Tag, both the word boundary and its POS tag have to be correctly identified to be counted as a correct token.
segmentations is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Haffari, Gholamreza and Sarkar, Anoop
Sentence Selection: Single Language Pair
where Hx is the space of all possible segmentations for the 00V fragment X, Y)?
Sentence Selection: Single Language Pair
We let Hx to be all possible segmentations of the fragment x for which the resulting phrase lengths are not greater than the maximum length constraint for phrase extraction in the underlying SMT model.
Sentence Selection: Single Language Pair
Since we do not know anything about the segmentations a priori, we have put a uniform distribution over such segmentations .
segmentations is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Huang, Jian and Taylor, Sarah M. and Smith, Jonathan L. and Fotiadis, Konstantinos A. and Giles, C. Lee
Experiments
We then used the SEG algorithm to learn the weight distribution model.
Methods 2.1 Document Level and Profile Based CDC
The chained entities 5 are first objectified into the relation strength matrix R using SEG , the details of which are described in the following section.
Methods 2.1 Document Level and Profile Based CDC
Algorithm 2 SEG (Freund et al., 1997) Input: Initial weight distribution p1; learning rate 77 > 0; training set {< st, 3/75 >} 1: for t=l to T do 2: Predict using:
Methods 2.1 Document Level and Profile Based CDC
We adopt the Specialist Exponentiated Gradient ( SEG ) (Freund et al., 1997) algorithm to learn the mixing weights of the specialists’ prediction (Algorithm 2) in an online manner.
segmentations is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Li, Zhifei and Eisner, Jason and Khudanpur, Sanjeev
Abstract
That is, the probability of an output string is split among many distinct derivations (e.g., trees or segmentations ).
Background 2.1 Terminology
(2003)), where different segmentations lead to the same translation string (Figure l), and in syntax-based systems (e.g., Chiang (2007)), where different derivation trees yield the same string (Figure 2).
Background 2.1 Terminology
Figure l: Segmentation ambiguity in phrase-based MT: two different segmentations lead to the same translation string.
segmentations is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Tofiloski, Milan and Brooke, Julian and Taboada, Maite
Data and Evaluation
use the coauthor’s segmentations as the gold standard.
Discussion
Also to be investigated is a quantitative study of the effects of high-precision/low-recall vs. low-precision/high-recall segmenters on the construction of discourse trees.
Results
Additionally, we compared SLSeg and SPADE to the original RST segmentations of the three RST texts taken from RST literature.
segmentations is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: