Distributional Analysis of Verbal Neologisms: Task Definition and Dataset Construction

In this paper we introduce the task of interpreting verbal neologism (VNeo) for the Italian language making use of a highly context-sensitive distributional semantic model (DSM). The task is commonly performed manually by lexicographers verifying the contexts in which the VNeo appear. Developing such a task is likely to be of use from a cognitive, social and linguistic perspective. In the following, we first outline the motivation for our study and our goal, then focus on the construction of the dataset and the definition of the task.In questo contributo introduciamo un task di interpretazione dei neologismi verbali (Vneo) in italiano, utilizzando un modello di semantica distribuzionale altamente sensibile al contesto. Questa attività è comunemente svolta manualmente dai lessicografi, i quali verificano il contesto in cui il Vneo appare. Sviluppare questo tipo di task può rivelarsi utile da una prospettiva linguistica, cognitiva e sociale. Di seguito presenteremo inizialmente le motivazioni e gli scopi dell’analisi, concentrandoci poi sulla costruzione del dataset e sulla definizione del task

A distributional semantic methodology for enhanced search in historical records: A case study on smell

In this paper we present a methodology based on distributional semantic models that can be flexibly adapted to the specific challenges posed by historical texts and that allow users to retrieve semantically relevant text without the need to close-read the documents. We focus on a case study concerned with detecting smell-related sentences in historical medical reports. We demonstrate a process for moving from generic domain label input to a more nuanced evaluation of the semantics of smell in a set of sentences extracted from this corpus, and then develop a machine learning technique for compounding scores on a variety of modelling parameters into more effective classifications.This work was supported by the Chist-ERA Atlantis project. This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1

Re-representing metaphor:Modeling metaphor perception using dynamically contextual distributional semantics

In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process

Right hemisphere has the last laugh: neural dynamics of joke appreciation

Understanding a joke relies on semantic, mnemonic, inferential, and emotional contributions from multiple brain areas. Anatomically constrained magnetoencephalography (aMEG) combining high-density whole-head MEG with anatomical magnetic resonance imaging allowed us to estimate where the humor-specific brain activations occur and to understand their temporal sequence. Punch lines provided either funny, not funny (semantically congruent), or nonsensical (incongruent) replies to joke questions. Healthy subjects rated them as being funny or not funny. As expected, incongruous endings evoke the largest N400m in left-dominant temporo-prefrontal areas, due to integration difficulty. In contrast, funny punch lines evoke the smallest N400m during this initial lexical–semantic stage, consistent with their primed “surface congruity” with the setup question. In line with its sensitivity to ambiguity, the anteromedial prefrontal cortex may contribute to the subsequent “second take” processing, which, for jokes, presumably reflects detection of a clever “twist” contained in the funny punch lines. Joke-selective activity simultaneously emerges in the right prefrontal cortex, which may lead an extended bilateral temporo-frontal network in establishing the distant unexpected creative coherence between the punch line and the setup. This progression from an initially promising but misleading integration from left frontotemporal associations, to medial prefrontal ambiguity evaluation and right prefrontal reprocessing, may reflect the essential tension and resolution underlying humor

Geometric Methods for Context Sensitive Distributional Semantics

PhDThis thesis describes a novel methodology, grounded in the distributional semantic paradigm, for building context sensitive models of word meaning, affording an empirical exploration of the relationship between words and concepts. Anchored in theoretical linguistic insight regarding the contextually specified nature of lexical semantics, the work presented here explores a range of techniques for the selection of subspaces of word co-occurrence dimensions based on a statistical analysis of input terms as observed within large-scale textual corpora. The relationships between word-vectors that emerge in the projected subspaces can be analysed in terms of a mapping between their geometric features and their semantic properties. The power of this modelling technique is its ability to generate ad hoc semantic relationships in response to an extemporaneous linguistic or conceptual situation. The product of this approach is a generalisable computational linguistic methodology, capable of taking input in various forms, including word groupings and sentential context, and dynamically generating output from a broad base model of word co-occurrence data. To demonstrate the versatility of the method, this thesis will present competitive empirical results on a range of established natural language tasks including word similarity and relatedness rating, metaphor and metonymy detection, and analogy completion. A range of techniques will be applied in order to explore the ways in which different aspects of projected geometries can be mapped to different semantic relationships, allowing for the discovery of a range of lexical and conceptual properties for any given input and providing a basis for an empirical exploration of distinctions between the semantic phenomena under analysis. The case made here is that the flexibility of these models and their ability to extend output to evaluations of unattested linguistic relationships constitutes the groundwork for a method for the extrapolation of dynamic conceptual relationships from large-scale textual corpora. This method is presented as a complement and a counterpoint to established distributional methods for generating lexically productive word-vectors. Where contemporary vector space models of distributional semantics have almost universally involved either the factorisation of co-occurrence matrices or the incremental learning of abstract representations using neural networks, the approach described in this thesis preserves the connection between the individual dimensions of word-vectors and statistics pertaining to observations in a textual corpus. The hypothesis tested here is that the maintenance of actual, interpretable information about underlying linguistic data allows for the contextual selection of non-normalised subspaces with more nuanced geometric features. In addition to presenting competitive results for various computational linguistic targets, the thesis will suggest that the transparency of its representations indicates scope for the application of this model to various real-world problems where an interpretable relationship between data and output is highly desirable. This, finally, demonstrates a way towards the productive application of the theory and philosophy of language to computational linguistic practice.Engineering and Physical Sciences Research Council of the UK, EP/L50483X/1

topicmodels: An R Package for Fitting Topic Models

Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics

In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process

Knowledge harvesting from text and web sources

Abstract-The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources has enabled the automatic construction of very large knowledge bases. Recent endeavors of this kind include academic research projects such as DBpedia, KnowItAll, Probase, ReadTheWeb, and YAGO, as well as industrial ones such as Freebase and Trueknowledge. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, deep question answering, and semantic search for entities and relations in Web and enterprise data. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-theart methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges