Julie Weeds


2020

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Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP
Ahmed Younes | Julie Weeds
Proceedings of the Fifth Arabic Natural Language Processing Workshop

Our research focuses on the potential improvements of exploiting language specific characteristics in the form of embeddings by neural networks. More specifically, we investigate the capability of neural techniques and embeddings to represent language specific characteristics in two sequence labeling tasks: named entity recognition (NER) and part of speech (POS) tagging. In both tasks, our preprocessing is designed to use enriched Arabic representation by adding diacritics to undiacritized text. In POS tagging, we test the ability of a neural model to capture syntactic characteristics encoded within these diacritics by incorporating an embedding layer for diacritics alongside embedding layers for words and characters. In NER, our architecture incorporates diacritic and POS embeddings alongside word and character embeddings. Our experiments are conducted on 7 datasets (4 NER and 3 POS). We show that embedding the information that is encoded in automatically acquired Arabic diacritics improves the performance across all datasets on both tasks. Embedding the information in automatically assigned POS tags further improves performance on the NER task.

2017

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Improving Semantic Composition with Offset Inference
Thomas Kober | Julie Weeds | Jeremy Reffin | David Weir
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.

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When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases
Julie Weeds | Thomas Kober | Jeremy Reffin | David Weir
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Non-compositional phrases such as red herring and weakly compositional phrases such as spelling bee are an integral part of natural language (Sag, 2002). They are also the phrases that are difficult, or even impossible, for good compositional distributional models of semantics. Compositionality detection therefore provides a good testbed for compositional methods. We compare an integrated compositional distributional approach, using sparse high dimensional representations, with the ad-hoc compositional approach of applying simple composition operations to state-of-the-art neural embeddings.

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One Representation per Word - Does it make Sense for Composition?
Thomas Kober | Julie Weeds | John Wilkie | Jeremy Reffin | David Weir
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf single-vector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remarkably well.

2016

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Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Thomas Kober | Julie Weeds | Jeremy Reffin | David Weir
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A critique of word similarity as a method for evaluating distributional semantic models
Miroslav Batchkarov | Thomas Kober | Jeremy Reffin | Julie Weeds | David Weir
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Aligning Packed Dependency Trees: A Theory of Composition for Distributional Semantics
David Weir | Julie Weeds | Jeremy Reffin | Thomas Kober
Computational Linguistics, Volume 42, Issue 4 - December 2016

2014

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Distributional Composition using Higher-Order Dependency Vectors
Julie Weeds | David Weir | Jeremy Reffin
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)

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Learning to Distinguish Hypernyms and Co-Hyponyms
Julie Weeds | Daoud Clarke | Jeremy Reffin | David Weir | Bill Keller
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2007

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Unsupervised Acquisition of Predominant Word Senses
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Computational Linguistics, Volume 33, Number 4, December 2007

2005

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Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
Julie Weeds | David Weir
Computational Linguistics, Volume 31, Number 4, December 2005

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The Distributional Similarity of Sub-Parses
Julie Weeds | David Weir | Bill Keller
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment

2004

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Finding Predominant Word Senses in Untagged Text
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Characterising Measures of Lexical Distributional Similarity
Julie Weeds | David Weir | Diana McCarthy
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Automatic Identification of Infrequent Word Senses
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Using automatically acquired predominant senses for Word Sense Disambiguation
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

2003

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A General Framework for Distributional Similarity
Julie Weeds | David Weir
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing