Tamara Polajnar


A Corpus of Very Short Scientific Summaries
Yifan Chen | Tamara Polajnar | Colin Batchelor | Simone Teufel
Proceedings of the 24th Conference on Computational Natural Language Learning

We present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain. These are one- or two-sentence author-written summaries that present the key findings of a paper. This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth. We introduce the dataset and evaluate it with state-of-the-art summarisation methods.


RELPRON: A Relative Clause Evaluation Data Set for Compositional Distributional Semantics
Laura Rimell | Jean Maillard | Tamara Polajnar | Stephen Clark
Computational Linguistics, Volume 42, Issue 4 - December 2016


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An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics
Tamara Polajnar | Laura Rimell | Stephen Clark
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

Low-Rank Tensors for Verbs in Compositional Distributional Semantics
Daniel Fried | Tamara Polajnar | Stephen Clark
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


Reducing Dimensions of Tensors in Type-Driven Distributional Semantics
Tamara Polajnar | Luana Fǎgǎrǎşan | Stephen Clark
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Evaluation of Simple Distributional Compositional Operations on Longer Texts
Tamara Polajnar | Laura Rimell | Stephen Clark
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Distributional semantic models have been effective at representing linguistic semantics at the word level, and more recently research has moved on to the construction of distributional representations for larger segments of text. However, it is not well understood how the composition operators that work well on short phrase-based models scale up to full-length sentences. In this paper we test several simple compositional methods on a sentence-length similarity task and discover that their performance peaks at fewer than ten operations. We also introduce a novel sentence segmentation method that reduces the number of compositional operations.

Improving Distributional Semantic Vectors through Context Selection and Normalisation
Tamara Polajnar | Stephen Clark
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics


UCAM-CORE: Incorporating structured distributional similarity into STS
Tamara Polajnar | Laura Rimell | Douwe Kiela
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity


User-friendly ontology authoring using a controlled language
Valentin Tablan | Tamara Polajnar | Hamish Cunningham | Kalina Bontcheva
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In recent years, following the rapid development in the Semantic Web and Knowledge Management research, ontologies have become more in demand in Natural Language Processing. An increasing number of systems use ontologies either internally, for modelling the domain of the application, or as data structures that hold the output resulting from the work of the system, in the form of knowledge bases. While there are many ontology editing tools aimed at expert users, there are very few which are accessible to users wishing to create simple structures without delving into the intricacies of knowledge representation languages. The approach described in this paper allows users to create and edit ontologies simply by using a restricted version of the English language. The controlled language described within is based on an open vocabulary and a restricted set of grammatical constructs. Sentences written in this language unambiguously map into a number of knowledge representation formats including OWL and RDF-S to allow round-trip ontology management.