In this work we address both problems by incorporating cross-lingual features and knowledge bases from English using cross—lingual links.

We show the effectiveness of cross-lingual features and resources on a standard dataset as well as on two new test sets that cover both news and microblogs.

To address this problem, we introduce the use of cross-lingual links between a disadvantaged language, Arabic, and a language with good discrim-inative features and large resources, English, to improve Arabic NER.

Cross-lingual links are obtained using Wikipedia cross-language links and a large Machine Translation (MT) phrase table that is true cased, where word casing is preserved during training.

- Using cross-lingual links to exploit orthographic features in other languages.

2.1 Using cross-lingual Features

If cross-lingual resources are available, such as parallel data, increased training data, better resources, or superior features can be used to improve the processing (ex.

To overcome these two problems, we use cross-lingual features to improve NER using large bilingual resources, and we incorporate confidences to avoid having a binary feature.

This approach is then evaluated on three language pairs, demonstrating competitive performance as compared to a state-of-the-art unsupervised SRL system and a cross-lingual annotation projection baseline.

Cross-lingual annotation projection systems (Pado and Lapata, 2009), for example, propagate information directly via word alignment links.

An alternative approach, known as cross-lingual model transfer, or cross-lingual model adaptation, consists of modifying a source-language model to make it directly applicable to a new language.

(2012) enriches this representation with cross-lingual word clusters, considerably improving the performance.

The purpose of the study is not to develop a yet another semantic role labeling system — any existing SRL system can (after some modification) be used in this setup — but to assess the practical applicability of cross-lingual model transfer to this problem, compare it against the alternatives and identify its strong/weak points depending on a particular setup.

With respect to the use of syntactic annotation we consider two options: using an existing dependency parser for the target language and obtaining one by means of cross-lingual transfer (see section 4.2).

This can be achieved either by means of cross-lingual annotation projection (Yarowsky et al., 2001) or by cross-lingual model transfer (Zeman and Resnik, 2008).

To show the usefulness of such a resource, we present a case study of cross-lingual transfer parsing with more reliable evaluation than has been possible before.

One of the motivating factors in creating such a data set was improved cross-lingual transfer evaluation.

First, a homogeneous representation is critical for multilingual language technologies that require consistent cross-lingual analysis for downstream components.

Second, consistent syntactic representations are desirable in the evaluation of unsupervised (Klein and Manning, 2004) or cross-lingual syntactic parsers (Hwa et al., 2005).

In the cross-lingual study of McDonald et al.

The selected sentences were preprocessed using cross-lingual taggers (Das and Petrov, 2011) and parsers (McDonald et al., 2011).

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

4.3 Approach 3: Cross-Lingual Clustering (XC)

(2012) introduced cross-lingual clustering.

In cross-lingual clustering, the objective function maximizes the joint likelihood of monolingual and cross-lingual factors.

Cross-lingual clustering for CLSA

Popular approaches for Cross-Lingual Sentiment Analysis (CLSA) (Wan, 2009; Duh et al., 2011) depend on Machine Translation (MT) for converting the labeled data from one language to the other (Hiroshi et al., 2004; Banea et al., 2008; Wan, 2009).

Instead, language gap for performing CLSA is bridged using linked cluster or cross-lingual clusters (explained in section 4) with the help of unlabelled monolingual corpora.

In situations where labeled data is not present in a language, approaches based on cross-lingual sentiment analysis are used.

Cross-lingual SA accuracies are presented in Table 3.

In this paper, we formulate the problem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called WikiCiKE, to solve this problem.

It obtains the values by two steps: finding their counterparts (if available) in English using Wikipedia cross-lingual links and attribute alignments, and translating them into Chinese.

Some translation-based cross-lingual knowledge

The recall of new target infoboxes is highly limited by the number of equivalent cross-lingual articles and the number of existing source infoboxes.

In general, we address the problem of cross-lingual knowledge extraction by using the imbalance between Wikipedias of different languages.

To generate the training data for the target attribute attrT, we first determine the equivalent cross-lingual attribute attrs.

Therefore, it is convenient to align the cross-lingual attributes using English Wikipedia as bridge.

In this section, we introduce some basic concepts regarding Wikipedia, formally defining the key problem of cross-lingual knowledge extraction and providing an overview of the WikiCiKE framework.

We will use “name in TEMPLATE PERSON” to refer to the attribute attrname in the template tp p E R30 N. In this cross-lingual task, we use the source (S) and target (T) languages to denote the languages of auxiliary and target Wikipcdias, rc-spcctivcly.

We introduce additional cross-lingual edge factors that encourage agreements between tagging and alignment decisions.

In order to model this uncertainty, we extend the two previously independent CRF models into a larger undirected graphical model, by introducing a cross-lingual edge factor gb(z', j ) for every pair of word positions (2', j) E A.

Initially, each of the cross-lingual edge factors will attempt to assign a pair of tags that has the highest PMI score, but if the monolingual taggers do not agree, a penalty will start accumulating over this pair, until some other pair that agrees better with the monolingual models takes the top spot.

Simultaneously, the monolingual models will also be encouraged to agree with the cross-lingual edge factors.

We introduce a cross-lingual edge factor C (i, j) in the undirected graphical model for every pair of word indices (2', j), which predicts a binary vari-

One special note is that after each iteration when we consider updates to the dual constraint for entity tags, we only check tag agreements for cross-lingual edge factors that have an alignment assignment value of 1.

In other words, cross-lingual edges that are not aligned do not affect bilingual NER tagging.

Cross-lingual information transfer

This need can be addressed in part by cross-lingual information access tasks such as entity linking (McNamee et al., 2011; Cassidy et al., 2012), event extraction (Hakkani-Tur et al., 2007), slot filling (Snover et al., 2011) and question answering (Parton et al., 2009; Parton and McKeown, 2010).

A key bottleneck of high-quality cross-lingual information access lies in the performance of Machine Translation (MT).

Traditional name tagging approaches for single languages cannot address this requirement because they were all built on data and resources which are specific to each language without using any cross-lingual features.

We developed a bilingual joint name tagger (Li et al., 2012) based on conditional random fields that incorporates both monolingual and cross-lingual features and conducts joint inference, so that name tagging from two languages can mutually enhance each other and therefore inconsistent results can be corrected simultaneously.

However, for cross-lingual information processing applications, we should acknowledge that certain informationally critical words are more important than other common words.

Postprocessing: in a cross-lingual information retrieval or question answering framework, online query names can be utilized to obtain translation and post-edit MT output (Parton et al., 2009; Ma and McKeown, 2009; Parton and McKeown, 2010; Parton et al., 2012).

Another issue that applies to all automatic (and also manual) approaches of cross-lingual FrameNet extension is the restricted cross-language applicability of frames.

Unlike corpus-based approaches for cross-lingual FrameNet extension, our approach does not provide frame-semantic annotations for the example

Example annotations can be additionally obtained via cross-lingual annotation projection (Pado and Lapata, 2009), and the lexical information in FNWKde can be used to guide this process.

Previous cross-lingual transfer of FrameNet used corpus-based approaches, or resource alignment with multilingual expert-built resources, such as EuroWordNet.

To our knowledge, Wiktionary has not been evaluated as an interlingual index for the cross-lingual extension of lexical-semantic resources.

In this vein, Pado and Lapata (2005) propose a cross-lingual FrameNet extension to German and French; J ohansson and Nugues (2005) and J ohansson and Nugues (2006) do this for Spanish and Swedish, and Basili et al.

It can also be viewed as a simplified version of the Cross-Lingual Lexical Substitution (Mihalcea et al., 2010) and Cross-Lingual Word Sense Disambiguation (Lefever and Hoste, 2010) tasks, as defined in SemEval-2010.

While LSI is more frequently used in information retrieval, the translation knowledge acquisition task can be recast as a cross-lingual indexing task, following (Dumais et al., 1997).

Basile and Semeraro (2010) also used Wikipedia articles as a parallel corpus for their participation in the SemEval 2010 Cross-Lingual Lexical Substitution task.

(2011) also tackled the problem of cross-lingual disambiguation for under-resourced language pairs (English—Persian) using Wikipedia articles, by applying the one sense per collocation and one sense per discourse heuristics on a comparable corpus.

Monolingual and cross-lingual textual entailment in particular would be interesting applications, because they require finding shared meaning on two text fragments.

3.2 Raw cross-lingual text categorization

3.4 Cross-lingual text categorization in a latent semantic space adding etymology

words) from the source to the target language, word etymologies are a novel source of cross-lingual knowledge.

We present an information theoretic obj ec-tive for bilingual word clustering that incorporates both monolingual distributional evidence as well as cross-lingual evidence from parallel corpora to learn high quality word clusters jointly in any number of languages.

(2012) use cross-lingual word clusters to show transfer of linguistic structure.

Also closely related is the technique of cross-lingual annotation projection.