The background documents consists of 2.7M running words, which was used to compute distributional similarity .

The context metric space was composed by k:-nearest neighbor words of distributional similarity (Lin, 1998), as is described in Section 4.

(2004), which determines the word sense based on sense similarity and distributional similarity to the k-nearest neighbor words of a target word by distributional similarity .

(2004) proposed a method to combine sense similarity with distributional similarity and configured predominant sense score.

Distributional similarity was used to weight the influence of context words, based on large-scale statistics.

(2009) used a k-nearest words on distributional similarity as context words.

Distributional similarity (Lin, 1998) was computed among target words, based on the statistics of the test set and the background text provided as the official dataset of the SemEval-2 English all-words task (Agirre et al., 2010).

Those texts were parsed using RASP parser (Briscoe et al., 2006) version 3.1, to obtain grammatical relations for the distributional similarity , as well as to obtain lemmata and part-of-speech (POS) tags which are required to look up the sense inventory of WordNet.

Based on the distributional similarity , we just used k-nearest neighbor words as the context of each target word.

Automatic acquisition of inference rules for predicates has been commonly addressed by computing distributional similarity between vectors of argument words, operating at the word space level.

learning, based on distributional similarity at the word level, and then context-sensitive scoring for rule applications, based on topic-level similarity.

The DIRT algorithm (Lin and Pantel, 2001) follows the distributional similarity paradigm to learn predicate inference rules.

In particular, we proposed a novel scheme that applies over any base distributional similarity measure which operates at the word level, and computes a single context-insensitive score for a rule.

We therefore focused on comparing the performance of our two-level scheme with state-of-the-art prior topic-level and word-level models of distributional similarity , over a random sample of inference rule applications.

Since our model can contextualize various distributional similarity measures, we evaluated the performance of all the above methods on several base similarity measures and their learned rule-

Whenever we evaluated a distributional similarity measure (namely Lin, BInc, or Cosine), we discarded instances from Zeichner et al.’s dataset in which the assessed rule is not in the context-insensitive rule-set learned for this measure or the argument instantiation of the rule is not in the LDA lexicon.

Specifically, topics are leveraged for high-level domain disambiguation, while fine grained word-level distributional similarity is computed for each rule under each such domain.

Indeed, on test-setvc, in which context mismatches are rare, our algorithm is still better than the original measure, indicating that WT can be safely applied to distributional similarity measures without concerns of reduced performance in different context scenarios.

On the other hand, the topic-biased similarity for 751 is substantially lower, since prominent words in this topic are likely to occur with ‘acquire’ but not with ‘learn’, yielding low distributional similarity .

rity: l) thesaurus-based word similarity, 2) distributional similarity and 3) confusion set derived from learner corpus.

Distributional Similarity : Thesaurus-based methods produce weak recall since many words, phrases and semantic connections are not covered by hand-built thesauri, especially for verbs and adjectives.

As an alternative, distributional similarity models are often used since it gives higher recall.

The focus of this paper is drawing nuanced, connotative sentiments from even those words that are objective on the surface, such as “intelligence”, “human”, and “cheesecake We propose induction algorithms encoding a diverse set of linguistic insights (semantic prosody, distributional similarity , semantic parallelism of coordination) and prior knowledge drawn from lexical resources, resulting in the first broad-coverage connotation lexicon.

The second subgraph is based on the distributional similarities among the arguments.

One possible way of constructing such a graph is simply connecting all nodes and assign edge weights proportionate to the word association scores, such as PMI, or distributional similarity .

where (meso‘ly is the scores based on semantic prosody, (1)0007"d captures the distributional similarity over coordination, and (13%“ controls the sensitivity of connotation detection between positive (negative) and neutral.

Therefore, in order to attain a broad coverage lexicon while maintaining good precision, we guide the induction algorithm with multiple, carefully selected linguistic insights: [1] distributional similarity , [2] semantic parallelism of coordination, [3] selectional preference, and [4] semantic prosody (e.g., Sinclair (1991), Louw (1993), Stubbs (1995), Stefanowitsch and Gries (2003))), and also exploit existing lexical resources as an additional inductive bias.