However, syntax-based methods can only use discrete contextual information, which may suffer from data sparsity .

Lexical semantic clue verifies whether a candidate term is related to the target product, and contextual semantic clue serves as a soft pattern miner to find candidates, which exploits semantics of each word in context so as to alleviate the data sparsity problem.

As for SGW-TSVM, the features they used for the TSVM suffer from the data sparsity problem for infrequent terms.

Therefore, such a representation often suffers from the data sparsity problem (Turian et al., 2010).

This enables our method to be less sensitive to lexicon change, so that the data sparsity problem can be alleviated .

As discussed in the first section, syntactic patterns often suffer from data sparsity .

Thus, the data sparsity problem can be alleviated.

To alleviate the data sparsity problem, EB is first trained on a very large corpus3 (denoted by C), and then fine-tuned on the target review corpus R. Particularly, for phrasal product features, a statistic-based method in (Zhu et al., 2009) is used to detect noun phrases in R. Then, an Unfolding Recursive Autoencoder (Socher et al., 2011) is trained on C to obtain embedding vectors for noun phrases.

Estimating tensors of this size runs into data sparseness issues already for less common transitive verbs.

Besides losing the comparability of the semantic contribution of a word across syntactic contexts, we also worsen the data sparseness issues.

Evidently, the separately-trained subject and object matrices of plf, being less affected by data sparseness than the 3-way tensors of If, are better able to capture how verbs interact with their arguments.

We expect a reasonably large corpus to feature many occurrences of a verb with a variety of subjects and a variety of objects (but not necessarily a variety of subjects with each of the objects as required by Grefenstette et al.’s training), allowing us to avoid the data sparseness issue.

This leads to a severe data sparsity problem even for moderately long sentences.

Assume for this section is large (we address the data sparsity issue in §3.4).

We now address the data sparsity problem, in particular that ’D(a:) can be very small, and therefore estimating d for each POS sequence separately can be problematic.3

Traditional models of distributional semantics suffer from computational issues such as data sparsity for individual lex-emes and complexities of modeling semantic composition when dealing with structures larger than single lexical items.

The framework subsumes issues such as differential compositional as well as non-compositional behavior of phrasal con-situents, and circumvents some problems of data sparsity by design.

Such a framework for distributional models avoids the issue of data sparsity in learning of representations for larger linguistic structures.