Paul Cook


2021

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Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages
Ali Hakimi Parizi | Paul Cook
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Cross-lingual word embeddings provide a way for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training. This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We consider bilingual lexicon induction, including an evaluation focused on OOVs. We find that this method achieves improvements over previous approaches, particularly for OOVs.

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UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders
Milton King | Ali Hakimi Parizi | Samin Fakharian | Paul Cook
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.

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Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)
Paul Cook | Jelena Mitrović | Carla Parra Escartín | Ashwini Vaidya | Petya Osenova | Shiva Taslimipoor | Carlos Ramisch
Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

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Contextualized Embeddings Encode Monolingual and Cross-lingual Knowledge of Idiomaticity
Samin Fakharian | Paul Cook
Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

Potentially idiomatic expressions (PIEs) are ambiguous between non-compositional idiomatic interpretations and transparent literal interpretations. For example, “hit the road” can have an idiomatic meaning corresponding to ‘start a journey’ or have a literal interpretation. In this paper we propose a supervised model based on contextualized embeddings for predicting whether usages of PIEs are idiomatic or literal. We consider monolingual experiments for English and Russian, and show that the proposed model outperforms previous approaches, including in the case that the model is tested on instances of PIE types that were not observed during training. We then consider cross-lingual experiments in which the model is trained on PIE instances in one language, English or Russian, and tested on the other language. We find that the model outperforms baselines in this setting. These findings suggest that contextualized embeddings are able to learn representations that encode knowledge of idiomaticity that is not restricted to specific expressions, nor to a specific language.

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Cross-Lingual Wolastoqey-English Definition Modelling
Diego Bear | Paul Cook
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Definition modelling is the task of automatically generating a dictionary-style definition given a target word. In this paper, we consider cross-lingual definition generation. Specifically, we generate English definitions for Wolastoqey (Malecite-Passamaquoddy) words. Wolastoqey is an endangered, low-resource polysynthetic language. We hypothesize that sub-word representations based on byte pair encoding (Sennrich et al., 2016) can be leveraged to represent morphologically-complex Wolastoqey words and overcome the challenge of not having large corpora available for training. Our experimental results demonstrate that this approach outperforms baseline methods in terms of BLEU score. 

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Now, It’s Personal : The Need for Personalized Word Sense Disambiguation
Milton King | Paul Cook
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet’s first senses, the predominant senses of our dataset’s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author’s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author’s sense distributions or predominant senses can greatly increase its performance.

2020

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Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora
Ali Hakimi Parizi | Paul Cook
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon. This method could be particularly well-suited to learning cross-lingual embeddings for lower-resource, morphologically-rich languages, enabling knowledge to be transferred from rich- to lower-resource languages. We evaluate our proposed approach simulating lower-resource languages for bilingual lexicon induction, monolingual word similarity, and document classification. Our results indicate that incorporating sub-word information indeed leads to improvements, and in the case of document classification, performance better than, or on par with, strong benchmark approaches.

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Evaluating Approaches to Personalizing Language Models
Milton King | Paul Cook
Proceedings of the 12th Language Resources and Evaluation Conference

In this work, we consider the problem of personalizing language models, that is, building language models that are tailored to the writing style of an individual. Because training language models requires a large amount of text, and individuals do not necessarily possess a large corpus of their writing that could be used for training, approaches to personalizing language models must be able to rely on only a small amount of text from any one user. In this work, we compare three approaches to personalizing a language model that was trained on a large background corpus using a relatively small amount of text from an individual user. We evaluate these approaches using perplexity, as well as two measures based on next word prediction for smartphone soft keyboards. Our results show that when only a small amount of user-specific text is available, an approach based on priming gives the most improvement, while when larger amounts of user-specific text are available, an approach based on language model interpolation performs best. We carry out further experiments to show that these approaches to personalization outperform language model adaptation based on demographic factors.

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Evaluating Sub-word Embeddings in Cross-lingual Models
Ali Hakimi Parizi | Paul Cook
Proceedings of the 12th Language Resources and Evaluation Conference

Cross-lingual word embeddings create a shared space for embeddings in two languages, and enable knowledge to be transferred between languages for tasks such as bilingual lexicon induction. One problem, however, is out-of-vocabulary (OOV) words, for which no embeddings are available. This is particularly problematic for low-resource and morphologically-rich languages, which often have relatively high OOV rates. Approaches to learning sub-word embeddings have been proposed to address the problem of OOV words, but most prior work has not considered sub-word embeddings in cross-lingual models. In this paper, we consider whether sub-word embeddings can be leveraged to form cross-lingual embeddings for OOV words. Specifically, we consider a novel bilingual lexicon induction task focused on OOV words, for language pairs covering several language families. Our results indicate that cross-lingual representations for OOV words can indeed be formed from sub-word embeddings, including in the case of a truly low-resource morphologically-rich language.

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Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi’kmaq Language Modelling
Jeremie Boudreau | Akankshya Patra | Ashima Suvarna | Paul Cook
Proceedings of the 12th Language Resources and Evaluation Conference

Mi’kmaq is an Indigenous language spoken primarily in Eastern Canada. It is polysynthetic and low-resource. In this paper we consider a range of n-gram and RNN language models for Mi’kmaq. We find that an RNN language model, initialized with pre-trained fastText embeddings, performs best, highlighting the importance of sub-word information for Mi’kmaq language modelling. We further consider approaches to language modelling that incorporate cross-lingual word embeddings, but do not see improvements with these models. Finally we consider language models that operate over segmentations produced by SentencePiece — which include sub-word units as tokens — as opposed to word-level models. We see improvements for this approach over word-level language models, again indicating that sub-word modelling is important for Mi’kmaq language modelling.

2019

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UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language
Ali Hakimi Parizi | Milton King | Paul Cook
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we apply a range of approaches to language modeling – including word-level n-gram and neural language models, and character-level neural language models – to the problem of detecting hate speech and offensive language. Our findings indicate that language models are able to capture knowledge of whether text is hateful or offensive. However, our findings also indicate that more conventional approaches to text classification often perform similarly or better.

2018

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Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
Ali Hakimi Parizi | Paul Cook
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.

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Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations
Milton King | Paul Cook
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Verb-noun combinations (VNCs) - e.g., blow the whistle, hit the roof, and see stars - are a common type of English idiom that are ambiguous with literal usages. In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations. Our results show that a model based on averaging word embeddings performs on par with, or better than, a previously-proposed approach based on skip-thoughts. Idiomatic usages of VNCs are known to exhibit lexico-syntactic fixedness. We further incorporate this information into our models, demonstrating that this rich linguistic knowledge is complementary to the information carried by distributed representations.

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UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
Milton King | Ali Hakimi Parizi | Paul Cook
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.

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Towards Language Technology for Mi’kmaq
Anant Maheshwari | Léo Bouscarrat | Paul Cook
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Supervised and unsupervised approaches to measuring usage similarity
Milton King | Paul Cook
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications

Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory. Instead, pairs of usages of a target lemma are rated on a scale. In this paper we propose unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that although they outperform unsupervised approaches, they are unable to generalize to lemmas that are unseen in the training data.

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Deep Learning Models For Multiword Expression Identification
Waseem Gharbieh | Virendrakumar Bhavsar | Paul Cook
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.

2016

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UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
Milton King | Waseem Gharbieh | SoHyun Park | Paul Cook
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Evaluating a Topic Modelling Approach to Measuring Corpus Similarity
Richard Fothergill | Paul Cook | Timothy Baldwin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Web corpora are often constructed automatically, and their contents are therefore often not well understood. One technique for assessing the composition of such a web corpus is to empirically measure its similarity to a reference corpus whose composition is known. In this paper we evaluate a number of measures of corpus similarity, including a method based on topic modelling which has not been previously evaluated for this task. To evaluate these methods we use known-similarity corpora that have been previously used for this purpose, as well as a number of newly-constructed known-similarity corpora targeting differences in genre, topic, time, and region. Our findings indicate that, overall, the topic modelling approach did not improve on a chi-square method that had previously been found to work well for measuring corpus similarity.

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Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus
SoHyun Park | Afsaneh Fazly | Annie Lee | Brandon Seibel | Wenjie Zi | Paul Cook
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for out-of-vocabulary terms. We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms. We show that the features based on word embeddings are particularly informative for this task. The categories that we predict could serve as a preliminary, automatically-generated source of lexical knowledge about out-of-vocabulary terms. Furthermore, we show that this approach can be adapted to give a semi-automated method for identifying out-of-vocabulary terms of a particular category, automotive named entities, that is of particular interest to us.

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Determining the Multiword Expression Inventory of a Surprise Language
Bahar Salehi | Paul Cook | Timothy Baldwin
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Much previous research on multiword expressions (MWEs) has focused on the token- and type-level tasks of MWE identification and extraction, respectively. Such studies typically target known prevalent MWE types in a given language. This paper describes the first attempt to learn the MWE inventory of a “surprise” language for which we have no explicit prior knowledge of MWE patterns, certainly no annotated MWE data, and not even a parallel corpus. Our proposed model is trained on a treebank with MWE relations of a source language, and can be applied to the monolingual corpus of the surprise language to identify its MWE construction types.

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A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations
Waseem Gharbieh | Virendra Bhavsar | Paul Cook
Proceedings of the 12th Workshop on Multiword Expressions

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Proceedings of the 10th Web as Corpus Workshop
Paul Cook | Stefan Evert | Roland Schäfer | Egon Stemle
Proceedings of the 10th Web as Corpus Workshop

2015

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Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data
Long Duong | Trevor Cohn | Steven Bird | Paul Cook
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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A Neural Network Model for Low-Resource Universal Dependency Parsing
Long Duong | Trevor Cohn | Steven Bird | Paul Cook
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions
Bahar Salehi | Paul Cook | Timothy Baldwin
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser
Long Duong | Trevor Cohn | Steven Bird | Paul Cook
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)

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The Impact of Multiword Expression Compositionality on Machine Translation Evaluation
Bahar Salehi | Nitika Mathur | Paul Cook | Timothy Baldwin
Proceedings of the 11th Workshop on Multiword Expressions

2014

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Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models
Jey Han Lau | Paul Cook | Diana McCarthy | Spandana Gella | Timothy Baldwin
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Using Distributional Similarity of Multi-way Translations to Predict Multiword Expression Compositionality
Bahar Salehi | Paul Cook | Timothy Baldwin
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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One Sense per Tweeter ... and Other Lexical Semantic Tales of Twitter
Spandana Gella | Paul Cook | Timothy Baldwin
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Novel Word-sense Identification
Paul Cook | Jey Han Lau | Diana McCarthy | Timothy Baldwin
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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What Can We Get From 1000 Tokens? A Case Study of Multilingual POS Tagging For Resource-Poor Languages
Long Duong | Trevor Cohn | Karin Verspoor | Steven Bird | Paul Cook
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Detecting Non-compositional MWE Components using Wiktionary
Bahar Salehi | Paul Cook | Timothy Baldwin
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Exploring Methods and Resources for Discriminating Similar Languages
Marco Lui | Ned Letcher | Oliver Adams | Long Duong | Paul Cook | Timothy Baldwin
Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects

2013

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Automatically Assessing Whether a Text Is Cliched, with Applications to Literary Analysis
Paul Cook | Graeme Hirst
Proceedings of the 9th Workshop on Multiword Expressions

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Classifying English Documents by National Dialect
Marco Lui | Paul Cook
Proceedings of the Australasian Language Technology Association Workshop 2013 (ALTA 2013)

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Simpler unsupervised POS tagging with bilingual projections
Long Duong | Paul Cook | Steven Bird | Pavel Pecina
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Stacking-based Approach to Twitter User Geolocation Prediction
Bo Han | Paul Cook | Timothy Baldwin
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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How Noisy Social Media Text, How Diffrnt Social Media Sources?
Timothy Baldwin | Paul Cook | Marco Lui | Andrew MacKinlay | Li Wang
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Increasing the Quality and Quantity of Source Language Data for Unsupervised Cross-Lingual POS Tagging
Long Duong | Paul Cook | Steven Bird | Pavel Pecina
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity
Spandana Gella | Bahar Salehi | Marco Lui | Karl Grieser | Paul Cook | Timothy Baldwin
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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Unsupervised Word Usage Similarity in Social Media Texts
Spandana Gella | Paul Cook | Bo Han
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages
Bahar Salehi | Paul Cook
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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unimelb: Topic Modelling-based Word Sense Induction for Web Snippet Clustering
Jey Han Lau | Paul Cook | Timothy Baldwin
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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unimelb: Topic Modelling-based Word Sense Induction
Jey Han Lau | Paul Cook | Timothy Baldwin
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Proceedings of the Australasian Language Technology Association Workshop 2012
Paul Cook | Scott Nowson
Proceedings of the Australasian Language Technology Association Workshop 2012

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langid.py for better language modelling
Paul Cook | Marco Lui
Proceedings of the Australasian Language Technology Association Workshop 2012

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Automatically Constructing a Normalisation Dictionary for Microblogs
Bo Han | Paul Cook | Timothy Baldwin
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Geolocation Prediction in Social Media Data by Finding Location Indicative Words
Bo Han | Paul Cook | Timothy Baldwin
Proceedings of COLING 2012

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Word Sense Induction for Novel Sense Detection
Jey Han Lau | Paul Cook | Diana McCarthy | David Newman | Timothy Baldwin
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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A Support Platform for Event Detection using Social Intelligence
Timothy Baldwin | Paul Cook | Bo Han | Aaron Harwood | Shanika Karunasekera | Masud Moshtaghi
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Automatic identification of words with novel but infrequent senses
Paul Cook | Graeme Hirst
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Book Review: A Way with Words: Recent Advances in Lexical Theory and Analysis: A Festschrift for Patrick Hanks edited by Gilles-Maurice de Schryver
Paul Cook
Computational Linguistics, Volume 37, Issue 2 - June 2011

2010

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Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity
Paul Cook | Anna Feldman
Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity

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No Sentence Is Too Confusing To Ignore
Paul Cook | Suzanne Stevenson
Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground

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Automatically Identifying Changes in the Semantic Orientation of Words
Paul Cook | Suzanne Stevenson
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The meanings of words are not fixed but in fact undergo change, with new word senses arising and established senses taking on new aspects of meaning or falling out of usage. Two types of semantic change are amelioration and pejoration; in these processes a word sense changes to become more positive or negative, respectively. In this first computational study of amelioration and pejoration we adapt a web-based method for determining semantic orientation to the task of identifying ameliorations and pejorations in corpora from differing time periods. We evaluate our proposed method on a small dataset of known historical ameliorations and pejorations, and find it to perform better than a random baseline. Since this test dataset is small, we conduct a further evaluation on artificial examples of amelioration and pejoration, and again find evidence that our proposed method is able to identify changes in semantic orientation. Finally, we conduct a preliminary evaluation in which we apply our methods to the task of finding words which have recently undergone amelioration or pejoration.

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Automatically Identifying the Source Words of Lexical Blends in English
Paul Cook | Suzanne Stevenson
Computational Linguistics, Volume 36, Number 1, March 2010

2009

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An Unsupervised Model for Text Message Normalization
Paul Cook | Suzanne Stevenson
Proceedings of the Workshop on Computational Approaches to Linguistic Creativity

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Unsupervised Type and Token Identification of Idiomatic Expressions
Afsaneh Fazly | Paul Cook | Suzanne Stevenson
Computational Linguistics, Volume 35, Number 1, March 2009

2007

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Pulling their Weight: Exploiting Syntactic Forms for the Automatic Identification of Idiomatic Expressions in Context
Paul Cook | Afsaneh Fazly | Suzanne Stevenson
Proceedings of the Workshop on A Broader Perspective on Multiword Expressions

2006

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Classifying Particle Semantics in English Verb-Particle Constructions
Paul Cook | Suzanne Stevenson
Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties