Bahar Salehi


2019

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Feature-guided Neural Model Training for Supervised Document Representation Learning
Aili Shen | Bahar Salehi | Jianzhong Qi | Timothy Baldwin
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

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Modelling Uncertainty in Collaborative Document Quality Assessment
Aili Shen | Daniel Beck | Bahar Salehi | Jianzhong Qi | Timothy Baldwin
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. To imitate people’s disagreement over inherently subjective tasks such as rating the quality of a Wikipedia article, a document quality assessment system should provide not only a prediction of the article quality but also the uncertainty over its predictions. This motivates us to measure the uncertainty in document quality predictions, in addition to making the label prediction. Experimental results show that both Gaussian processes (GPs) and random forests (RFs) can yield competitive results in predicting the quality of Wikipedia articles, while providing an estimate of uncertainty when there is inconsistency in the quality labels from the Wikipedia contributors. We additionally evaluate our methods in the context of a semi-automated document quality class assignment decision-making process, where there is asymmetric risk associated with overestimates and underestimates of document quality. Our experiments suggest that GPs provide more reliable estimates in this context.

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How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions
Navnita Nandakumar | Timothy Baldwin | Bahar Salehi
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.

2018

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A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions
Navnita Nandakumar | Bahar Salehi | Timothy Baldwin
Proceedings of the Australasian Language Technology Association Workshop 2018

In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions("MWEs"). Our experimental results suggest that character- and document-level models capture knowledge of MWE compositionality and are effective in modelling varying levels of compositionality, with the advantage over word-level models that they do not require token-level identification of MWEs in the training corpus.

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Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
Reza Haffari | Colin Cherry | George Foster | Shahram Khadivi | Bahar Salehi
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP

2017

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Evaluating hypotheses in geolocation on a very large sample of Twitter
Bahar Salehi | Anders Søgaard
Proceedings of the 3rd Workshop on Noisy User-generated Text

Recent work in geolocation has made several hypotheses about what linguistic markers are relevant to detect where people write from. In this paper, we examine six hypotheses against a corpus consisting of all geo-tagged tweets from the US, or whose geo-tags could be inferred, in a 19% sample of Twitter history. Our experiments lend support to all six hypotheses, including that spelling variants and hashtags are strong predictors of location. We also study what kinds of common nouns are predictive of location after controlling for named entities such as dolphins or sharks

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Huntsville, hospitals, and hockey teams: Names can reveal your location
Bahar Salehi | Dirk Hovy | Eduard Hovy | Anders Søgaard
Proceedings of the 3rd Workshop on Noisy User-generated Text

Geolocation is the task of identifying a social media user’s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help. However, not all content features are equally revealing of a user’s location. In this paper, we evaluate nine name entity (NE) types. Using various metrics, we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for geolocation than other NE types. Using these types, we improve geolocation accuracy and reduce distance error over various famous text-based methods.

2016

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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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UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures
Timothy Baldwin | Huizhi Liang | Bahar Salehi | Doris Hoogeveen | Yitong Li | Long Duong
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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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

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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

2014

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Automatic Identification of Expressions of Locations in Tweet Messages using Conditional Random Fields
Fei Liu | Afshin Rahimi | Bahar Salehi | Miji Choi | Ping Tan | Long Duong
Proceedings of the Australasian Language Technology Association Workshop 2014

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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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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)

2013

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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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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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Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity Features
Yvette Graham | Bahar Salehi | Timothy Baldwin
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)