Index of papers in Proc. ACL 2008 that mention
  • unigram
Andreevskaia, Alina and Bergler, Sabine
Experiments
Consistent with findings in the literature (Cui et al., 2006; Dave et al., 2003; Gamon and Aue, 2005), on the large corpus of movie review texts, the in-domain-trained system based solely on unigrams had lower accuracy than the similar system trained on bigrams.
Experiments
On sentences, however, we have observed an inverse pattern: unigrams performed better than bigrams and trigrams.
Experiments
Due to lower frequency of higher-order n-grams (as opposed to unigrams ), higher-order n-gram language models are more sparse, which increases the probability of missing a particular sentiment marker in a sentence (Table 33).
Factors Affecting System Performance
System runs with unigrams , bigrams, and trigrams as features and with different training set sizes are presented.
Lexicon-Based Approach
One of the limitations of general lexicons and dictionaries, such as WordNet (Fellbaum, 1998), as training sets for sentiment tagging systems is that they contain only definitions of individual words and, hence, only unigrams could be effectively learned from dictionary entries.
Lexicon-Based Approach
Since the structure of WordNet glosses is fairly different from that of other types of corpora, we developed a system that used the list of human-annotated adjectives from (Hatzivassiloglou and McKeown, 1997) as a seed list and then learned additional unigrams
unigram is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Chan, Yee Seng and Ng, Hwee Tou
Automatic Evaluation Metrics
Then, unigram matching is performed on the remaining words that are not matched using paraphrases.
Automatic Evaluation Metrics
Based on the matches, ParaEval will then elect to use either unigram precision or unigram recall as its score for the sentence pair.
Automatic Evaluation Metrics
Based on the number of word or unigram matches and the amount of string fragmentation represented by the alignment, METEOR calculates a score for the pair of strings.
unigram is mentioned in 24 sentences in this paper.
Topics mentioned in this paper:
Johnson, Mark
Word segmentation with adaptor grammars
Figure l: The unigram word adaptor grammar, which uses a unigram model to generate a sequence of words, where each word is a sequence of phonemes.
Word segmentation with adaptor grammars
3.1 Unigram word adaptor grammar
Word segmentation with adaptor grammars
(2007a) presented an adaptor grammar that defines a unigram model of word segmentation and showed that it performs as well as the unigram DP word segmentation model presented by (Goldwater et al., 2006a).
unigram is mentioned in 25 sentences in this paper.
Topics mentioned in this paper:
Szarvas, Gy"orgy
Conclusions
Our finding that token unigram features are capable of solving the task accurately agrees with the the results of previous works on hedge classification ((Light et al., 2004), (Med-
Methods
For trigrams, bigrams and unigrams — processed separately — we calculated a new class-conditional probability for each feature cc, discarding those observations of c in speculative instances where c was not among the two highest ranked candidate.
Results
About half of these were the kind of phrases that had no unigram components of themselves in the feature set, so these could be regarded as meaningful standalone features.
Results
Our model using just unigram features achieved a BEP(spec) score of 78.68% and F5=1(spec) score of 80.23%, which means that using bigram and trigram hedge cues here significantly improved the performance (the difference in BEP (spec) and F5=1(spec) scores were 5.23% and 4.97%, respectively).
Results
Our experiments revealed that in radiology reports, which mainly concentrate on listing the identified diseases and symptoms (facts) and the physician’s impressions (speculative parts), detecting hedge instances can be performed accurately using unigram features.
unigram is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Fleischman, Michael and Roy, Deb
Evaluation
The remaining 93 unlabeled games are used to train unigram , bigram, and trigram grounded language models.
Evaluation
Only unigrams , bigrams, and tri—grams that are not proper names, appear greater than three times, and are not composed only of stop words were used.
Evaluation
with traditional unigram , bigram, and trigram language models generated from a combination of the closed captioning transcripts of all training games and data from the switchboard corpus (see below).
Linguistic Mapping
3 In the discussion that follows, we describe a method for estimating unigram grounded language models.
unigram is mentioned in 5 sentences in this paper.
Topics mentioned in this paper: