Index of papers in Proc. ACL 2013 that mention
  • word pairs
Biran, Or and McKeown, Kathleen
Abstract
We present a reformulation of the word pair features typically used for the task of disambiguating implicit relations in the Penn Discourse Treebank.
Abstract
Our word pair features achieve significantly higher performance than the previous formulation when evaluated without additional features.
Introduction
Without an explicit marker to rely on, work on this task initially focused on using lexical cues in the form of word pairs mined from large corpora where they appear around an explicit marker (Marcu and Echihabi, 2002).
Introduction
The intuition is that these pairs will tend to represent semantic relationships which are related to the discourse marker (for example, word pairs often appearing around but may tend to be antonyms).
Introduction
While this approach showed some success and has been used extensively in later work, it has been pointed out by multiple authors that many of the most useful word pairs
Related Work
This line of research began with (Marcu and Echihabi, 2002), who used a small number of unambiguous explicit markers and patterns involving them, such as [Arg1, but Arg2] to collect sets of word pairs from a large corpus using the crossproduct of the words in Argl and Arg2.
Related Work
Second, it is constructed with the same unsupervised method they use to extract the word pairs -by assuming that the patterns correspond to a particular relation and collecting the arguments from an unannotated corpus.
Related Work
They used word pairs as well as additional features to train four binary classifiers, each corresponding to one of the high-level PDTB relation classes.
word pairs is mentioned in 36 sentences in this paper.
Topics mentioned in this paper:
Yang, Nan and Liu, Shujie and Li, Mu and Zhou, Ming and Yu, Nenghai
DNN for word alignment
In contrast, our model does not maintain a separate translation score parameters for every source-target word pair , but computes tlegc through a multilayer network, which naturally handles contexts on both sides without explosive growth of number of parameters.
DNN for word alignment
The example computes translation score for word pair (yibula, yibulayin) given its surrounding context.
DNN for word alignment
For word pair (61', fj), we take fixed length windows surrounding both 6, and fj as input: (€i_%, .
Introduction
As shown in example (a) of Figure 1, in word pair {“juda” =>“mammot ”}, the Chinese word “juda” is a common word, but
Introduction
For example (b) in Figure l, for the word pair {“yibula” => “Yibula”}, both the Chinese word “yibula” and English word “Yibula” are rare name entities, but the words around them are very common, which are {“nongmin”, “shuo”} for Chinese side and {“farmer”, “said”} for the English side.
Introduction
The pattern of the context {“nongmin X shuo” => “farmer X said”} may help to align the word pair which fill the variable X, and also, the pattern {“yixiang X gongcheng” => “a X job”} is helpful to align the word pair {“juda” =>“mammoth”} for example (a).
Training
ma${071 _ t9((ev f)+|e7 + t9((ev f)—|e7 (10) where (e, f)+ is a correct word pair, (6, f)‘ is a wrong word pair in the same sentence, and 759 is as defined in Eq.
Training
This training criteria essentially means our model suffers loss unless it gives correct word pairs a higher score than random pairs from the same sentence pair with some margin.
Training
We randomly cycle through all sentence pairs in training data; for each correct word pair (including null alignment), we generate a positive example, and generate two negative examples by randomly corrupting either
word pairs is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Beigman Klebanov, Beata and Flor, Michael
Application to Essay Scoring
We calculated correlations between essay score and the proportion of word pairs in each of the 60 bins of the WAP histogram, separately for each of the prompts p1-p6 in setA.
Application to Essay Scoring
Next, observe the consistent negative correlations between essay score and the proportion of word pairs in bins PMI=0.833 through PMI=1.5.
Conclusion
We hypothesize that this pattern is consistent with the better essays demonstrating both a better topic development (hence the higher percentage of highly related pairs) and a more creative use of language resources, as manifested in a higher percentage of word pairs that generally do not tend to appear together.
Illustration: The shape of the distribution
Yet, the picture at the right tail is remarkably similar to that of the essays, with 9% of word pairs , on average, having PMI>2.17.
Illustration: The shape of the distribution
The right tail, with PMI>2.17, holds 19% of all word pairs in these texts — more than twice the proportion in essays written by college graduates or in texts from the WSJ.
Introduction
fact that a text segmentation algorithm that uses information about patterns of word co-occurrences can detect subtopic shifts in a text (Riedl and Bie-mann, 2012; Misra et al., 2009; Eisenstein and Barzilay, 2008) tells us that texts contain some proportion of more highly associated word pairs (those in subsequent sentences within the same topical unit) and of less highly associated pairs (those in sentences from different topical units).1 Yet, does each text have a different distribution of highly associated, mildly associated, unassoci-ated, and disassociated pairs of words, or do texts tend to strike a similar balance of these?
Methodology
The third decision is how to represent the co-occurrence profiles; we use a histogram where each bin represents the proportion of word pairs in the given interval of PMI values.
Methodology
The lowest bin (shown in Figures 1 and 2 as PMI = —5) contains pairs with PMIg—S; the topmost bin (shown in Figures 1 and 2 as PMI = 4.83) contains pairs with PMI > 4.67, while the rest of the bins contain word pairs (:c,y) with —5 <PMI(x,y) g 4.67.
Methodology
Thus, the text “The dog barked and wagged its tail” is much tighter than the text “Green ideas sleep furiously”, with all the six content word pairs scoring above PMI=5.5 in the first and below PMI=2.2 in the second.4
Related Work
Our results suggest that this direction is promising, as merely the proportion of highly associated word pairs is already contributing a clear signal regarding essay quality; it is possible that additional information can be derived from richer representations common in the lexical cohesion literature.
word pairs is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Mohtarami, Mitra and Lan, Man and Tan, Chew Lim
Abstract
Sentiment Similarity of word pairs reflects the distance between the words regarding their underlying sentiments.
Abstract
This paper aims to infer the sentiment similarity between word pairs with respect to their senses.
Abstract
The resultant emotional vectors are then employed to infer the sentiment similarity of word pairs .
Analysis and Discussions
For this purpose, we repeat the experiment for SO prediction by computing sentiment similarity of word pairs with and without using synonyms and antonyms.
Introduction
In this paper, we show that sentiment similarity between word pairs can be effectively utilized to compute SO of words.
Introduction
0 We propose an effective approach to predict the sentiment similarity between word pairs through hidden emotions at the sense level,
Related Works
Most previous works employed semantic similarity of word pairs to address SO prediction and IQAP inference tasks.
Sentiment Similarity through Hidden Emotions
As we discussed above, semantic similarity measures are less effective to infer sentiment similarity between word pairs .
word pairs is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Yih, Wen-tau and Chang, Ming-Wei and Meek, Christopher and Pastusiak, Andrzej
Experiments
In other words, the IDF values help decide the importance of word pairs to the model.
Experiments
4 to the word pair and use their estimated degree of synonymy, antonymy, hyponymy and semantic relatedness as features.
Experiments
5, the features for the whole questiorflsentence pair are the average and max of features of all the word pairs .
Learning QA Matching Models
It then aggregates features extracted from each of these word pairs to represent the whole questiorflsentence pair.
Learning QA Matching Models
Given a word pair (wq,w8), where mg 6 Vq and ws 6 V8, feature functions o1, - -- ,gbd map it to a d-dimensional real-valued feature vector.
word pairs is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Feng, Song and Kang, Jun Seok and Kuznetsova, Polina and Choi, Yejin
Connotation Induction Algorithms
We experimented with many different variations on the graph structure and edge weights, including ones that include any word pairs that occurred frequently enough together.
Connotation Induction Algorithms
R59”: word pairs in synonyms relation.
Connotation Induction Algorithms
Ram: word pairs in antonyms relation.
word pairs is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Liu, Kai and Lü, Yajuan and Jiang, Wenbin and Liu, Qun
Related work
The Word Pair Classification (WPC) method (J iang and Liu, 2010) modifies the DPA method and makes it more robust.
Unsupervised Dependency Grammar Induction
denotes the word pair dependency relationship (e;- —> 63-).
Unsupervised Dependency Grammar Induction
Based on the features around deij, we can calculate the probability Pr(y|deij) that the word pair dew.
Unsupervised Dependency Grammar Induction
where y is the category of the relationship of dew: y = + means it is the probability that the word pair deij can form a dependency arc and y = —means the contrary.
word pairs is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Background
is one of the few examples where distributional representations are used for word pairs .
Experiments
The task is thus to rank these pairs of word pairs by their semantic similarity.
Experiments
We assume fixed parse trees for all of the compounds (Figure 6), and use these to compute compound level vectors for all word pairs .
word pairs is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Morita, Hajime and Sasano, Ryohei and Takamura, Hiroya and Okumura, Manabu
Joint Model of Extraction and Compression
Although the authors of QSB also provided scores of word pairs to avoid putting excessive penalties
Joint Model of Extraction and Compression
on word overlaps, we do not score word pairs .
Joint Model of Extraction and Compression
The score function is supermodular as a score function of subtree extraction3, because the union of two subtrees can have extra word pairs that are not included in either subtree.
word pairs is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Pilehvar, Mohammad Taher and Jurgens, David and Navigli, Roberto
A Unified Semantic Representation
Commonly, semantic comparisons are between word pairs or sentence pairs that do not have their lexical content sense-annotated, despite the potential utility of sense annotation in making semantic comparisons.
Experiment 2: Word Similarity
The dataset contains 65 word pairs judged by 51 human subjects on a scale of 0 to 4 according to their semantic similarity.
Introduction
Third, we demonstrate that this single representation can achieve state-of-the-art performance on three similarity tasks, each operating at a different lexical level: (1) surpassing the highest scores on the SemEval-2012 task on textual similarity (Agirre et al., 2012) that compares sentences, (2) achieving a near-perfect performance on the TOEFL synonym selection task proposed by Landauer and Dumais (1997), which measures word pair similarity, and also obtaining state-of-the-art performance in terms of the correlation with human judgments on the RG-65 dataset (Rubenstein and Goodenough, 1965), and finally (3) surpassing the performance of Snow et al.
word pairs is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: