Prominent questions about the role of sensory vs. linguistic input in the way we acquire and use language have been extensively studied in the psycholinguistic literature.However, the relative effect of various factors in a person's overall experience on their linguistic system remains unclear.We study this question by making a step forward towards a better understanding of the conceptual perception of colors by color-blind individuals, as reflected in their spontaneous linguistic productions.Using a novel and carefully curated dataset, we show that red-green color-blind speakers use the "red" and "green" color terms in less predictable contexts, and in linguistic environments evoking mental image to a lower extent, when compared to their normal-sighted counterparts.These findings shed some new and interesting light on the role of sensory experience on our linguistic system.

Dialog is a core building block of human natural language interactions.It contains multiparty utterances used to convey information from one party to another in a dynamic and evolving manner.The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center calls and virtual agent design.We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity.Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants.We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets.The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.

A large body of research on gender-linked language has established foundations regarding crossgender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings.We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers' gender, and perform a first large-scale empirical study of distinctions in the usage of figurative language between male and female authors.Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men's and women's idiomatic usages express higher emotion than their literal language, with detectable, albeit more subtle, differences between male and female authors along the dimension of dominance compared to similar distinctions in their literal utterances, and (3) contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases.

Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level.We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency.We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence on the nature of the challenges posed by indefinite pronouns to English learners.We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.

Ella Rabinovich, Masih Sultani, Suzanne Stevenson. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English).Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task.We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.

Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

We present a computational analysis of cognate effects on the spontaneous linguistic productions of advanced non-native speakers. Introducing a large corpus of highly competent non-native English speakers, and using a set of carefully selected lexical items, we show that the lexical choices of non-natives are affected by cognates in their native language. This effect is so powerful that we are able to reconstruct the phylogenetic language tree of the Indo-European language family solely from the frequencies of specific lexical items in the English of authors with various native languages. We quantitatively analyze non-native lexical choice, highlighting cognate facilitation as one of the important phenomena shaping the language of non-native speakers.

Translation has played an important role in trade, law, commerce, politics, and literature for thousands of years.Translators have always tried to be invisible; ideal translations should look as if they were written originally in the target language.We show that traces of the source language remain in the translation product to the extent that it is possible to uncover the history of the source language by looking only at the translation.Specifically, we automatically reconstruct phylogenetic language trees from monolingual texts (translated from several source languages).The signal of the source language is so powerful that it is retained even after two phases of translation.This strongly indicates that source language interference is the most dominant characteristic of translated texts, overshadowing the more subtle signals of universal properties of translation.

Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, Shuly Wintner. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

We present a computational analysis of three language varieties: native, advanced non-native, and translation. Our goal is to investigate the similarities and differences between non-native language productions and translations, contrasting both with native language. Using a collection of computational methods we establish three main results: (1) the three types of texts are easily distinguishable; (2) nonnative language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, reflecting, perhaps, unified principles that similarly affect translations and non-native language.

There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.

Work on using relevance feedback for retrieval has focused on the single retrieved list setting. That is, an initial document list is retrieved in response to the query and feedback for the most highly ranked documents is used to perform a second search. We address a setting wherein the list for which feedback is provided results from fusing several intermediate retrieved lists. Accordingly, we devise methods that utilize the feedback while exploiting the special characteristics of the fusion setting. Specifically, the feedback serves two different, yet complementary, purposes. The first is to directly rank the pool of documents in the intermediate lists. The second is to estimate the effectiveness of the intermediate lists for improved re-fusion. In addition, we present a meta fusion method that uses the feedback for these two purposes simultaneously. Empirical evaluation demonstrates the merits of our approach. As a case in point, the retrieval performance is substantially better than that of using the relevance feedback as in the single list setting. The performance also substantially transcends that of a previously proposed approach to utilizing relevance feedback in fusion-based retrieval.

We propose to develop a framework which provides the ability to apply complex event processing in realtime domains, thus allowing an easier process of developing and maintaining specific solutions for real-time event-based systems, while upholding the real time requirements of the system. Specifically, we propose to develop a framework that includes an integrated development environment for defining rules, and, given a set of rules, generates code for a complex event processing application for which it is able to determine time bounds on the response of this application to a set of supported events. In particular, the tool helps determine a time bound for the execution time of the code corresponding to each rule. Many Service Oriented Architecture (SOA) applications, in domains such as financial services, manufacturing, gaming and military/aerospace, have real-time performance requirements. We present real-life industry use cases from these domains as motivation for the potential benefit in developing real-time complex event processing applications. In support of a feasibility argument for the proposed approach we present some preliminary experimental results obtained on a partially implemented tool.