You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing 
Chapter Title:  Overview of Multilingual NLP 
1. Abstract of Cited Paper (BIB001):  A topic model outputs a set of multinomial distributions over words for each topic. In this paper, we investigate the value of bilingual topic models, i.e., a bilingual Latent Dirichlet Allocation model for finding translations of terms in comparable corpora without using any linguistic resources. Experiments on a document-aligned English-Italian Wikipedia corpus confirm that the developed methods which only use knowledge from word-topic distributions outperform methods based on similarity measures in the original word-document space. The best results, obtained by combining knowledge from word-topic distributions with similarity measures in the original space, are also reported. 
2. Abstract of Cited Paper (BIB002):  It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%.

Literature Review:
The scarcity of data and resources in many languages represents a major challenge for multilingual NLP. Many state-of-the-art methods rely on supervised learning, hence their performance depends on the availability of manually crafted data sets annotated with linguistic information (e.g., treebanks, parallel corpora) and/or lexical databases (e.g., terminology databases, dictionaries). Although similar resources are available for key tasks in a few well-researched languages, the majority of the world's languages lack them almost entirely. This gap cannot be easily bridged: The creation of linguistic resources is a time-consuming process and requires skilled labor. Furthermore, the immense range of possible tasks and languages makes the aim of a complete coverage unrealistic. One solution to this problem explored by the research community abandons the use of annotated resources altogether and instead focuses on unsupervised learning. This class of methods infers probabilistic models of the observations given some latent variables. In other words, it unravels the hidden structures within unlabeled text data. Although these methods have been used extensively for multilingual applications BIB001 BIB002 Titov and Klementiev 2012, inter alia) , their performance tends to lag behind the more linguistically informed supervised learning approaches BIB003 . Moreover, they have been rarely combined with typological knowledge. For these reasons, we do not review them in this section. Other promising ways to overcome data scarcity include transferring models or data from resource-rich to resource-poor languages ( § 3.1) or learning joint models from annotated examples in multiple languages ( § 3.2) in order to leverage language interdependencies. Early approaches of this kind have relied on universal, high-level delexicalized features, such as part of speech (PoS) tags and dependency relations. More recently, however, the incompatibility of (language-specific) lexica has been countered by mapping equivalent words into the same multilingual semantic space through representation learning ( § 3.3). This has enriched language transfer and multilingual joint