Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of-the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org

Language acquisition in humans relies on abilities like abstraction and use of syntactic rules, which are absent in other animals. The neural correlate of acquiring new linguistic competence was investigated with two functional magnetic resonance imaging (fMRI) studies. German native speakers learned a sample of 'real' grammatical rules of different languages (Italian or Japanese), which, although parametrically different, follow the universal principles of grammar (UG). Activity during this task was compared with that during a task that involved learning 'unreal' rules of language. 'Unreal' rules were obtained manipulating the original two languages; they used the same lexicon as Italian or Japanese, but were linguistically illegal, as they violated the principles of UG. Increase of activation over time in Broca's area was specific for 'real' language acquisition only, independent of the kind of language. Thus, in Broca's area, biological constraints and language experience interact to enable linguistic competence for a new language.

Language assessment has a crucial role in the clinical diagnosis of several neurodegenerative diseases. The analysis of extended speech production is a precious source of information encompassing the phonetic, phonological, lexico-semantic, morpho-syntactic, and pragmatic levels of language organization. The knowledge about the distinctive linguistic variables identifying language deficits associated to different neurodegenerative diseases has progressively improved in the last years. However, the heterogeneity of such variables and of the way they are measured and classified limits any generalization and makes the comparison among studies difficult. Here we present an exhaustive review of the studies focusing on the linguistic variables derived from the analysis of connected speech samples, with the aim of characterizing the language disorders of the most prevalent neurodegenerative diseases, including primary progressive aphasia, Alzheimer's disease, movement disorders, and amyotrophic lateral sclerosis. A total of 61 studies have been included, considering only those reporting group analysis and comparisons with a group of healthy persons. This review first analyzes the differences in the tasks used to elicit connected speech, namely picture description, story narration, and interview, considering the possible different contributions to the assessment of different linguistic domains. This is followed by an analysis of the terminologies and of the methods of measurements of the variables, indicating the need for harmonization and standardization. The final section reviews the linguistic domains affected by each different neurodegenerative disease, indicating the variables most consistently impaired at each level and suggesting the key variables helping in the differential diagnosis among diseases. While a large amount of valuable information is already available, the review highlights the need of further work, including the development of automated methods, to take advantage of the richness of connected speech analysis for both research and clinical purposes.

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In this paper we present the Multilingual AllWords Sense Disambiguation and Entity Linking task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems in the Natural Language Processing field and both address the lexical ambiguity of language. Their main difference lies in the kind of meaning inventories that are used: EL uses encyclopedic knowledge, while WSD uses lexicographic information. Our aim with this task is to analyze whether, and if so, how, using a resource that integrates both kinds of inventories (i.e., BabelNet 2.5.1) might enable WSD and EL to be solved by means of similar (even, the same) methods. Moreover, we investigate this task in a multilingual setting and for some specific domains.

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Language serves as a cornerstone of human cognition. However, our knowledge about its neural basis is still a matter of debate, partly because ‘language’ is often ill-defined. Rather than equating language with ‘speech’ or ‘communication’, we propose that language is best described as a biologically determined computational cognitive mechanism that yields an unbounded array of hierarchically structured expressions. The results of recent brain imaging studies are consistent with this view of language as an autonomous cognitive mechanism, leading to a view of its neural organization, whereby language involves dynamic interactions of syntactic and semantic aspects represented in neural networks that connect the inferior frontal and superior temporal cortices functionally and structurally. Our conceptions of the neural mechanisms of language have developed in tandem with our understanding of the nature of the language faculty as a cognitive system. Initially, research focused on frontal and temporal cortical regions as being involved in vocal production and speech perception, respectively. Since speech is the main medium of language used for communication, it may seem natural to equate language with speech or even ‘acoustic communication’1. This view, however, is too narrow. Speech is just one possible way of externalizing language (with sign or writing being other examples), ancillary to the internal computational system. In addition, ‘communication’ is merely a possible function of the language faculty, and cannot be equated with it. We argue that language is a species- and domain-specific human cognitive capacity (Box 1)2,3,4,5,6. In essence, language is an internal computational mechanism that yields an unbounded array of structured phrases and sentences. These must be minimally interpreted at two interfaces—that is, internal thoughts on the one hand, and externalization via sounds, writing or signs on the other (Box 1)4,5,7,8. Neurolinguistics focuses on the study of the neural substrates underlying the computational cognitive mechanism that lies at the core of human language. From a theoretical linguistic standpoint—that of generative grammar—language is posited to be a process described at a formal level, divided into functionally separable or autonomous components, such as syntax, morphology, and so on. The immediate question of interest that then arises is whether the formal representations exploited in generative grammar correspond to actual brain architecture. We will discuss independent lines of research converging on the result that syntactic processes are in fact independently computed in the brain

One of the most remarkable abilities of bilinguals is to produce and/or to perceive a switch from one language to the other without any apparent difficulty. However, several psycholinguistic studies indicate that producing, recognizing, and integrating a linguistic code different from the one in current use may entail a processing cost for the speaker/listener. Up to now, the underlying neural substrates of perceiving language switches are unknown. In the present study, we investigated the neural mechanisms of language switching during auditory perception in bilinguals. Event-related functional magnetic resonance imaging was performed in 12 early, highly proficient Italian/French bilinguals, who were more exposed to their second language. Subjects had to listen to narratives containing "switched passages" that could either respect (i.e., regular switches) or violate (i.e., irregular switches) the constituents of sentence structure. The results indicate that switching engages an extensive neural network, including bilateral prefrontal and temporal associative regions. Moreover, a clear dissociation is observed for the types of switches. Regular switches entail a pattern of brain activity closely related to lexical processing, whereas irregular switches engage brain structures involved in syntactic and phonological aspects of language processing. Noteworthy, when switching into the less-exposed language, we observed the selective engagement of subcortical structures and of the anterior cingulate cortex, putatively involved in cognitive and executive control. This suggests that switching into a less-exposed language requires controlled processing resources. This pattern of brain activity may constitute an important neural signature of language dominance in bilinguals.