Inclusions from other languages can be a significant source of errors for monolingual parsers . We show this for English inclusions , which are sufficiently frequent to present a problem when parsing German . We describe an annotation-free approach for accurately detecting such inclusions , and develop two methods for interfacing this approach with a state-of-the-art parser for German . An evaluation on the TIGER corpus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection , and even outperforms a parser with access to gold standard part-of-speech tags . The status of English as a global language means that English words and phrases are frequently borrowed by other languages , especially in domains such as science and technology , commerce , advertising , and current affairs . This is an instance of language mixing , whereby inclusions from other languages appear in an otherwise monolingual text . While the processing of foreign inclusions has received some attention in the text-to-speech ( TTS ) literature ( see Section 2 ) , the natural language processing ( NLP ) community has paid little attention both to the problem of inclusion detection , and to potential applications thereof . Also the extent to which inclusions pose a problem to existing NLP methods has not been investigated . In this paper , we address this challenge . We focus on English inclusions in German text . Anglicisms and other borrowings from English form by far the most frequent foreign inclusions in German . In specific domains , up to 6.4 % of the tokens of a German text can be English inclusions . Even in regular newspaper text as used for many NLP applications , English inclusions can be found in up to 7.4 % of all sentences ( see Section 3 for both figures ) . Virtually all existing NLP algorithms assume that the input is monolingual , and does not contain foreign inclusions . It is possible that this is a safe assumption , and inclusions can be dealt with accurately by existing methods , without resorting to specialized mechanisms . The alternative hypothesis , however , seems more plausible : foreign inclusions pose a problem for existing approaches , and sentences containing them are processed less accurately . A parser , for example , is likely to have problems with inclusions -most of the time , they are unknown words , and as they originate from another language , standard methods for unknown words guessing ( suffix stripping , etc . ) are unlikely to be successful . Furthermore , the fact that inclusions are often multiword expressions ( e.g. , named entities ) means that simply part-of-speech ( POS ) tagging them accurately is not sufficient : if the parser posits a phrase boundary within an inclusion this is likely to severely decrease parsing accuracy . In this paper , we focus on the impact of English inclusions on the parsing of German text . We describe an annotation-free method that accurately recognizes English inclusions , and demonstrate that inclusion detection improves the performance of a state-of-the-art parser for German . We show that the way of interfacing the inclusion detection and the parser is crucial , and propose a method for modifying the underlying probabilistic grammar in order to enable the parser to process inclusions accurately . This paper is organized as follows . We review related work in Section 2 , and present the English inclusion classifier in Section 3 . Section 4 describes our results on interfacing inclusion detection with parsing , and Section 5 presents an error analysis . Discussion and conclusion follow in Section 6 . This paper has argued that English inclusions in German text is an increasingly pervasive instance of language mixing . Starting with the hypothesis that such inclusions can be a significant source of errors for monolingual parsers , we found evidence that an unmodified state-of-the-art parser for Ger- man performs substantially worse on a set of sentences with English inclusions compared to a set of length-matched sentences randomly sampled from the same corpus . The lower performance on the inclusion set persisted even when the parser when given gold standard POS tags in the input . To overcome the poor accuracy of parsing inclusions , we developed two methods for interfacing the parser with an existing annotation-free inclusion detection system . The first method restricts the POS tags for inclusions that the parser can assign to those found in the data . The second method applies tree transformations to ensure that inclusions are treated as phrases . An evaluation on the TIGER corpus shows that the second method yields a performance gain of 4.3 in F-score over a baseline of no inclusion detection , and even outperforms a model involving perfect POS tagging of inclusions . To summarize , we have shown that foreign inclusions present a problem for a monolingual parser . We also demonstrated that it is insufficient to know where inclusions are or even what their parts of speech are . Parsing performance only improves if the parser also has knowledge about the structure of the inclusions . It is particularly important to know when adjacent foreign words are likely to be part of the same phrase . As our error analysis showed , this prevents cascading errors further up in the parse tree . Finally , our results indicate that future work could improve parsing performance for inclusions further : we found that parsing the inclusion set is still harder than parsing a randomly sampled test set , even for our best-performing model . This provides an upper bound on the performance we can expect from a parser that uses inclusion detection . Future work will also involve determining the English inclusion classifier 's merit when applied to rule-based parsing .

Challenge: While expressions in English as the international language appear in many other languages, how it impacts the monolingual parsing accuracy has not been investigated.
Approach: They describe an annotation-free method that recognizes English in German texts coupled with two methods for interfacing it with parsers.
Outcome: They show that English is frequently present in German texts to prevent accurate parsing even with gold part-of-speech tags but the proposed methods can improve.