Query Expansion with Locally-Trained Word Embeddings

Dı́az, Fernando; Mitra, Bhaskar; Craswell, Nick

doi:10.18653/v1/p16-1035

Cited by 195 publications

(187 citation statements)

References 45 publications

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“…We also compare the results of the proposed approach with a state-of-the-art query expansion approach based on locally trained embeddings [12]. This approach can possibly deal with the problem of multiple degrees of similarity by training the word embeddings on only the top-1000 documents.…”

Section: Resultsmentioning

confidence: 99%

“…However, the on-line computational overhead could be an issue in practice since the word embeddings are trained on a per-query basis. As only nDCG@10 is used in [12], Table 13 compares the best nDCG@10 reported in [12] with our approach on each of the three publicly available TREC collections. From the comparison results we can see that our method consistently has better results over [12] on all three TREC test collections.…”

Section: Resultsmentioning

confidence: 99%

“…As only nDCG@10 is used in [12], Table 13 compares the best nDCG@10 reported in [12] with our approach on each of the three publicly available TREC collections. From the comparison results we can see that our method consistently has better results over [12] on all three TREC test collections. Moreover, it can be observed that the Paragraph Embedding method outperforms Term Summation in most cases.…”

Section: Resultsmentioning

confidence: 99%

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A Feedback-Based Approach to Utilizing Embeddings for Clinical Decision Support

Yang

et al. 2017

Data Sci. Eng.

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Clinical Decision Support (CDS) is widely seen as an information retrieval (IR) application in the medical domain. The goal of CDS is to help physicians find useful information from a collection of medical articles with respect to the given patient records, in order to take the best care of their patients. Most of the existing CDS methods do not sufficiently consider the semantic relation between texts, hence the potential in improving the performance in biomedical articles retrieval. This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set. Evaluation results show that our method outperforms the strong baselines and is able to improve over the best runs in the TREC CDS tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A Feedback-Based Approach to Utilizing Embeddings for Clinical Decision Support

Yang

et al. 2017

Data Sci. Eng.

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“…Word embeddings have shown its great practicable usability in plenty of natural language processing tasks, such as information retrieval (Diaz et al, 2016;Zuccon et al, 2015), machine translation (Shi et al, 2016;Zhang et al, 2014), sentiment analysis Xu et al, 2015; and so on. Bilingual word embeddings have been induced for cross-lingual NLP tasks (Vulić and Moens, 2015;Guo et al, 2014;Zou et al, 2013;.…”

Section: Related Workmentioning

confidence: 99%

Towards a Universal Sentiment Classifier in Multiple languages

Xu¹,

Wan

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Existing sentiment classifiers usually work for only one specific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple different languages. In order to achieve this goal, we propose to learn multilingual sentiment-aware word embeddings simultaneously based only on the labeled reviews in English and unlabeled parallel data available in a few language pairs. It is not required that the parallel data exist between English and any other language, because the sentiment information can be transferred into any language via pivot languages. We present the evaluation results of our universal sentiment classifier in five languages, and the results are very promising even when the parallel data between English and the target languages are not used. Furthermore, the universal single classifier is compared with a few cross-language sentiment classifiers relying on direct parallel data between the source and target languages, and the results show that the performance of our universal sentiment classifier is very promising compared to that of different crosslanguage classifiers in multiple target languages.

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“…In this session we will focus on semantic matching settings where a supervised signal is available. The signal can be explicit, such as a label for learning task-speci c latent representations [25-27, 36, 47, 48], or relevance labels and, more implicitly, clicks for neural IR methods [15,19,31,41,42]. …”

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confidence: 99%

Neural Networks for Information Retrieval

Kenter¹,

Borisov

Gysel

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to many di erent approaches for many di erent IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research. It covers key architectures, as well as the most promising future directions. MOTIVATIONPrompted by the advances of deep learning in computer vision research, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being applied to all key parts of the typical modern IR pipeline, such core ranking algorithms [26,42,51], click models [9,10], knowledge graphs [8,35], text similarity [28,47], entity retrieval [52,53], language modeling [5], question answering [22,56], and dialogue systems [34,54].A key advantage that sets neural networks apart from many learning strategies employed earlier, is their ability to work from raw input data. E.g., when given enough training data, well-designed networks can become feature extractors themselves, e.g., incorporating basic input characteristics such as term frequency (tf) and term saliency (idf)-that used to be pre-calculated o ine-in their initial layers. Where designing features used to be a crucial aspect and contribution of newly proposed IR approaches, the focus * Corresponding author.SIGIR '17, Shinjuku, Tokyo, Japan has shifted to designing network architectures instead. As a consequence, many di erent architectures and paradigms have been proposed, such as auto-encoders, recursive networks, recurrent networks, convolutional networks, various embedding methods, deep reinforcement and deep q-learning, and, more recently, generative adversarial networks, of which most have been applied in IR settings. The aim of the neural networks for IR (NN4IR) tutorial is to provide a clear overview of the main network architectures currently applied in IR and to show explicitly how they relate to previous work. The tutorial covers methods applied in industry and academia, with in-depth insights into the underlying theory, core IR tasks, applicability, key assets and handicaps, scalability concerns and practical tips and tricks.We expect the tutorial to be useful both for academic and industrial researchers and practitioners who either want to develop new neural models, use them in their own research in other areas or apply the models described here to improve actual IR systems. OBJECTIVESThe material in the tutorial covers a broad range of IR applications. It is structured as follows:Preliminaries (60 minutes). The rece...

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Query Expansion with Locally-Trained Word Embeddings

Cited by 195 publications

References 45 publications

A Feedback-Based Approach to Utilizing Embeddings for Clinical Decision Support

A Feedback-Based Approach to Utilizing Embeddings for Clinical Decision Support

Towards a Universal Sentiment Classifier in Multiple languages

Neural Networks for Information Retrieval

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