A plausible reason for such a performance improvement is the reduction in data sparsity .

In this paper, the problem of data sparsity in sentiment analysis, both monolingual and cross-lingual, is addressed through the means of clustering.

Experiments show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level.

Data sparsity is the bane of Natural Language Processing (NLP) (Xue et al., 2005; Minkov et al., 2007).

NLP applications innovatively handle data sparsity through various means.

A special, but very common kind of data sparsity viz, word sparsity, can be addressed in one of the two obvious ways: 1) sparsity reduction through paradigmatically related words or 2) sparsity reduction through syntagmatically related words.

This paper presents an unsupervised random walk approach to alleviate data sparsity for selectional preferences.

However, this strategy is infeasible for many plausible triples due to data sparsity .

Then how to use a smooth model to alleviate data sparsity for SP?

Random walk models have been successfully applied to alleviate the data sparsity issue on collaborative filtering in recommender systems.

The damp factor d E (0, l), and its value mainly depends on the data sparsity level.

Experiments show it is efficient and effective to address data sparsity for SP.

The performance is comparable to entity grid based approaches though these rely on a computationally expensive training phase and face data sparsity problems.

Second, as it relies only on graph centrality, our model does not suffer from the computational complexity and data sparsity problems mentioned by Barzilay and Lapata (2008).

This can be easily done by adding edges in the projection graphs when sentences contain entities related from a discourse point of view while Lin et al.’s approach suffers from complexity and data sparsity problems similar to the entity grid model.

However, their approach has some disadvantages which they point out themselves: data sparsity , domain dependence and computational complexity, especially in terms of feature space issues while building their model (Barzilay and Lapata (2008, p.8, p.10, p.30), Elsner and Charniak (2011, p.126, p.127)).

The graph can easily span the entire text without leading to computational complexity and data sparsity problems.

From this we conclude that a graph is an alternative to the entity grid model: it is computationally more tractable for modeling local coherence and does not suffer from data sparsity problems (Section 5).

This is a major cause of data sparseness for corpus-based approaches to lexical semantics, such as distributional semantic models of word meaning.

This result is of practical importance for distributional semantics, as it paves the way to address one of the main causes of data sparseness , and it confirms the usefulness of the compositional approach in a new domain.

Not surprisingly, there is a strong correlation between word frequency and vector quality (Bullinaria and Levy, 2007), and since most words occur only once even in very large corpora (Baroni, 2009), DSMs suffer data sparseness .

Compositional distributional semantic models (cDSMs) of word units aim at handling, compositionally, the high productivity of phrases and consequent data sparseness .

Besides alleviating data sparseness problems, a system of this sort, that automatically induces the semantic contents of morphological processes, would also be of tremendous theoretical interest, given that the semantics of derivation is a central and challenging topic in linguistic morphology (Dowty, 1979; Lieber, 2004).

Morphological induction has recently received considerable attention since morphological analysis can mitigate data sparseness in domains such as parsing and machine translation (Goldberg and Tsarfaty, 2008; Lee, 2004).

The reason is that matrix factorization used in the paper can effectively solve the data sparseness and noise introduced by the machine translator simultaneously.

Our proposed method (SMT + MF) can effectively solve the data sparseness and noise via matrix factorization.

To further investigate the impact of the matrix factorization, one intuitive way is to expand the original questions with the translated words from other four languages, without considering the data sparseness and noise introduced by machine translator.

To tackle the data sparseness of question representation with the translated words, we hope to find two or more lower dimensional matrices whose product provides a good approximate to the original one via matrix factorization.

If we set a small value for Ap, the objective function behaves like the traditional NMF and the importance of data sparseness is emphasized; while a big value of Ap indicates Vp should be very closed to V1, and equation (3) aims to remove the noise introduced by statistical machine translation.

The objective function 0 defined in equation (4) performs data sparseness and noise removing simultaneously.

We observed that product-hierarchies did not performed well without cutting (especially when using longer sequences of indicators, because of data sparsity ) and could obtain scores lower than the single model.

The small number of outputs of an indicator is required for practical reasons: if a category of pairs is too refined, the associated feature space will suffer from data sparsity .

In effect, we want to learn the “best” subspaces for our different models: that is, subspaces that are neither too coarse (i.e., unlikely to separate the data well) nor too specific (i.e., prone to data sparseness and noise).

In practice, we have to cope with data sparsity : there will not be enough data to properly train a linear model on such a space.

Standard methods for part-of-speech tagging suffer from data sparseness when used on highly inflectional languages (which require large lexical tagset inventories).

The standard tagging methods, using such large tagsets, face serious data sparseness problems due to lack of statistical evidence, manifested by the non-robustness of the language models.

The previously proposed methods still suffer from the same issue of data sparseness when applied to MSD tagging.