87,951 research outputs found
Learning when to point : a data-driven approach
The relationship between how people describe objects and when they choose to point is complex
and likely to be influenced by factors related to both perceptual and discourse context. In this
paper, we explore these interactions using machine-learning on a dialogue corpus, to identify
multimodal referential strategies that can be used in automatic multimodal generation. We show
that the decision to use a pointing gesture depends on features of the accompanying description
(especially whether it contains spatial information), and on visual properties, especially distance
or separation of a referent from its previous referent.peer-reviewe
Inter-Coder Agreement for Computational Linguistics
This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasksâbut that their use makes the interpretation of the value of the coefficient even harder. </jats:p
Editorial for the First Workshop on Mining Scientific Papers: Computational Linguistics and Bibliometrics
The workshop "Mining Scientific Papers: Computational Linguistics and
Bibliometrics" (CLBib 2015), co-located with the 15th International Society of
Scientometrics and Informetrics Conference (ISSI 2015), brought together
researchers in Bibliometrics and Computational Linguistics in order to study
the ways Bibliometrics can benefit from large-scale text analytics and sense
mining of scientific papers, thus exploring the interdisciplinarity of
Bibliometrics and Natural Language Processing (NLP). The goals of the workshop
were to answer questions like: How can we enhance author network analysis and
Bibliometrics using data obtained by text analytics? What insights can NLP
provide on the structure of scientific writing, on citation networks, and on
in-text citation analysis? This workshop is the first step to foster the
reflection on the interdisciplinarity and the benefits that the two disciplines
Bibliometrics and Natural Language Processing can drive from it.Comment: 4 pages, Workshop on Mining Scientific Papers: Computational
Linguistics and Bibliometrics at ISSI 201
A Neural Model for Compositional Word Embeddings and Sentence Processing
We propose a new neural model for word embeddings, which uses Unitary Matrices as the primary device for encoding lexical information. It uses simple matrix multiplication to derive matrices for large units, yielding a sentence processing model that is strictly compositional, does not lose information over time steps, and is transparent, in the sense that word embed- dings can be analysed regardless of context. This model does not employ activation functions, and so the network is fully accessible to analysis by the methods of linear algebra at each point in its operation on an input sequence. We test it in two NLP agreement tasks and obtain rule like perfect accuracy, with greater stability than current state-of-the-art systems. Our proposed model goes some way towards offer- ing a class of computationally powerful deep learning systems that can be fully understood and compared to human cognitive processes for natural language learning and representation
Linguistic Markers of Influence in Informal Interactions
There has been a long standing interest in understanding `Social Influence'
both in Social Sciences and in Computational Linguistics. In this paper, we
present a novel approach to study and measure interpersonal influence in daily
interactions. Motivated by the basic principles of influence, we attempt to
identify indicative linguistic features of the posts in an online knitting
community. We present the scheme used to operationalize and label the posts
with indicator features. Experiments with the identified features show an
improvement in the classification accuracy of influence by 3.15%. Our results
illustrate the important correlation between the characteristics of the
language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for
Computational Linguistics) 201
Corpora for Computational Linguistics
Since the mid 90s corpora has become very important for computational linguistics. This paper offers a survey of how they are currently used in different fields of the discipline, with particular emphasis on anaphora and coreference resolution, automatic summarisation and term extraction.
Their influence on other fields is also briefly discussed
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