Nut Limsopatham


2018

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Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper
Nut Limsopatham | Oleg Rokhlenko | David Carmel
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

Recent advances in automatic speech recognition lead toward enabling a voice conversation between a human user and an intelligent virtual assistant. This provides a potential foundation for developing artificial personal shoppers for e-commerce websites, such as Alibaba, Amazon, and eBay. Personal shoppers are valuable to the on-line shops as they enhance user engagement and trust by promptly dealing with customers’ questions and concerns. Developing an artificial personal shopper requires the agent to leverage knowledge about the customer and products, while interacting with the customer in a human-like conversation. In this position paper, we motivate and describe the artificial personal shopper task, and then address a research agenda for this task by adapting and advancing existing information retrieval and natural language processing technologies.

2017

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Vancouver Welcomes You! Minimalist Location Metonymy Resolution
Milan Gritta | Mohammad Taher Pilehvar | Nut Limsopatham | Nigel Collier
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.

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Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
Leon Derczynski | Eric Nichols | Marieke van Erp | Nut Limsopatham
Proceedings of the 3rd Workshop on Noisy User-generated Text

This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?!” – even human experts find the entity ‘kktny’ hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.

2016

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Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation
Nut Limsopatham | Nigel Collier
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification
Nut Limsopatham | Nigel Collier
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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Bidirectional LSTM for Named Entity Recognition in Twitter Messages
Nut Limsopatham | Nigel Collier
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.

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Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition
Nut Limsopatham | Nigel Collier
Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)

End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that hand-engineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets.

2015

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Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
Nut Limsopatham | Nigel Collier
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing