Index of papers in Proc. ACL 2010 that mention
  • co-occurrence
Shezaf, Daphna and Rappoport, Ari
Algorithm
where Pr(w1, 2122) is the co-occurrence count, and Pr(wi) is the total number of appearance of w,-in the corpus (Church and Hanks, 1990).
Conclusion
Our results confirm that alignment is problematic in using co-occurrence methods across languages, at least in our settings.
Introduction
While co-occurrence scores are used to compute signatures, signatures, unlike context vectors, do not contain the score values.
Lexicon Generation Experiments
In the case of context vectors, the vector indices, or keys, are words, and their values are co-occurrence based scores.
Lexicon Generation Experiments
The window size for co-occurrence counting, k, was 4.
Lexicon Generation Experiments
In the three co-occurrence based methods, NAS similarity, cosine distance and and city block distance, the highest ranking translation was selected.
Previous Work
(2009) replaced the traditional window-based co-occurrence counting with dependency-tree based counting, while Pekar et al.
Previous Work
(2006) predicted missing co-occurrence values based on similar words in the same language.
co-occurrence is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Han, Xianpei and Zhao, Jun
Experiments
There were three knowledge sources we used for our experiments: the WordNet 3.0; the Sep. 9, 2007 English version of Wikipedia; and the Web pages of each ambiguous name in WePS datasets as the NE Co-occurrence Corpus.
Introduction
This model measures similarity based on only the co-occurrence statistics of terms, without considering all the semantic relations like social relatedness between named entities, associative relatedness between concepts, and lexical relatedness (e.g., acronyms, synonyms) between key terms.
Related Work
(2007) used the co-occurrence statistics between named entities in the Web.
The Structural Semantic Relatedness Measure
We extract three types of semantic relations (semantic relatedness between Wikipedia concepts, lexical relatedness between WordNet concepts and social relatedness between NEs) correspondingly from three knowledge sources: Wikipedia, WordNet and NE Co-occurrence Corpus.
The Structural Semantic Relatedness Measure
NE Co-occurrence Corpus, a corpus of documents for capturing the social relatedness between named entities.
The Structural Semantic Relatedness Measure
According to the fuzzy set theory (Baeza-Yates et al., 1999), the degree of named entities co-occurrence in a corpus is a measure of the relatedness between them.
co-occurrence is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Hassan, Ahmed and Radev, Dragomir R.
Experiments
The spin model approach uses word glosses, WordNet synonym, hypernym, and antonym relations, in addition to co-occurrence statistics extracted from corpus.
Experiments
Adding co-occurrence statistics slightly improved performance, while using glosses did not help at all.
Experiments
No glosses or co-occurrence statistics are used.
Related Work
To get co-occurrence statistics, they submit several queries to a search engine.
Related Work
They construct a network of words using gloss definitions, thesaurus, and co-occurrence statistics.
Word Polarity
Another source of links between words is co-occurrence statistics from corpus.
Word Polarity
We study the effect of using co-occurrence statistics to connect words later at the end of our experiments.
co-occurrence is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Jurgens, David and Stevens, Keith
The S-Space Framework
We divide the algorithms into four categories based on their structural similarity: document-based, co-occurrence , approximation, and Word Sense Induction (WSI) models.
The S-Space Framework
Co-occurrence models build the vector space using the distribution of co-occurring words in a context, which is typically defined as a region around a word or paths rooted in a parse tree.
The S-Space Framework
co-occurrence data rather than model it explicitly in order to achieve better scalability for larger data sets.
Word Space Models
Later models have expanded the notion of co-occurrence but retain the premise that distributional similarity can be used to extract meaningful relationships between words.
Word Space Models
Common approaches use a lexical distance, syntactic relation, or document co-occurrence to define the context.
Word Space Models
Co-occurrence Models HAL (Burgess and Lund, 1997) COALS (Rohde et a1., 2009)
co-occurrence is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Thater, Stefan and Fürstenau, Hagen and Pinkal, Manfred
Experiments: Ranking Paraphrases
As for the full model, we use pmi values rather than raw frequency counts as co-occurrence statistics.
Introduction
In the standard approach, word meaning is represented by feature vectors, with large sets of context words as dimensions, and their co-occurrence frequencies as values.
Introduction
This allows us to model the semantic interaction between the meaning of a head word and its dependent at the microlevel of relation-specific co-occurrence frequencies.
Related Work
Figure l: Co-occurrence graph of a small sample corpus of dependency trees.
The model
The basis for the construction of both kinds of vector representations are co-occurrence graphs.
The model
Figure 1 shows the co-occurrence graph of a small sample corpus of dependency trees: Words are represented as nodes in the graph, possible dependency relations between them are drawn as labeled edges, with weights corresponding to the observed frequencies.
The model
introduce another kind of vectors capturing infor-mations about all words that can be reached with two steps in the co-occurrence graph.
co-occurrence is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Mitchell, Jeff and Lapata, Mirella and Demberg, Vera and Keller, Frank
Models of Processing Difficulty
To give a concrete example, Latent Semantic Analysis (LSA, Landauer and Dumais 1997) creates a meaning representation for words by constructing a word-document co-occurrence matrix from a large collection of documents.
Models of Processing Difficulty
Like LSA, ICD is based on word co-occurrence vectors, however it does not employ singular value decomposition, and constructs a word-word rather than a word-document co-occurrence matrix.
Models of Processing Difficulty
Importantly, composition models are not defined with a specific semantic space in mind, they could easily be adapted to LSA, or simple co-occurrence vectors, or more sophisticated semantic representations (e.g., Griffiths et al.
co-occurrence is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Ó Séaghdha, Diarmuid
Experimental setup
Following their description, we use a 2,000-dimensional space of syntactic co-occurrence features appropriate to the relation being predicted, weight features with the G2 transformation and compute similarity with the cosine measure.
Results
30 predicates were selected for each relation; each predicate was matched with three arguments from different co-occurrence bands in the BNC, e.g., naughty-girl (high frequency), naughty-dog (medium) and naughty-lunch (low).
Three selectional preference models
Further differences are that information about predicate-argument co-occurrence is only shared within a given interaction class rather than across the whole dataset and that the distribution (Dz is not specific to the predicate 2) but rather to the relation 7“.
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: