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1
February 2017
ACM SIGIR Forum: Volume 50 Issue 2, December 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 13, Downloads (12 Months): 21, Downloads (Overall): 21
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The SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR) took place on 21 July, 2016 in Pisa. The goal of the Neu-IR (pronounced "New IR") workshop was to serve as a forum for academic and industrial researchers, working at the intersection of information retrieval (IR) and machine learning, to present ...
2
February 2017
Information Retrieval: Volume 20 Issue 1, February 2017
Publisher: Kluwer Academic Publishers
Query recommendation has long been considered a key feature of search engines, which can improve users' search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user's initial query. However, the ultimate ...
Keywords:
Query recommendation, Query utility, Search behavior, Dynamic Bayesian network
3
January 2017
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers: Volume 8 Issue 3, April 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 13, Downloads (12 Months): 84, Downloads (Overall): 84
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The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification . Recently, the relational learning to rank model, which designs a learnable ranking function following ...
Keywords:
Search result diversification, relational learning to rank, diversity evaluation measure
4
December 2016
Neurocomputing: Volume 218 Issue C, December 2016
Publisher: Elsevier Science Publishers B. V.
Search engine query recommendation based on mining query logs has been considered as an important and useful method of facilitating users to retrieve information. However, the log data evolves quickly. Existing query recommendation approaches have to rebuild the models when new log data arrive. In this paper, we extend the ...
Keywords:
Convex optimization, Query log analysis, Query recommendation, Incremental update method
5
October 2016
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 16, Downloads (12 Months): 125, Downloads (Overall): 125
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A common limitation of many information retrieval (IR) models is that relevance scores are solely based on exact (i.e., syntactic) matching of words in queries and documents under the simple Bag-of-Words (BoW) representation. This not only leads to the well-known vocabulary mismatch problem, but also does not allow semantically related ...
Keywords:
retrieval model, word embedding, word transportation
6
October 2016
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 51, Downloads (12 Months): 343, Downloads (Overall): 343
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As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features ...
Keywords:
term importance learning, value-shared weights, question answering, deep learning
7
October 2016
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 6, Downloads (12 Months): 59, Downloads (Overall): 59
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Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from been realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the ...
Keywords:
directed acyclic graph, dataflow, machine learning process
8
October 2016
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 86, Downloads (12 Months): 441, Downloads (Overall): 441
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In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc ...
Keywords:
semantic matching, ad-hoc retrieval, neural models, relevance matching, ranking models
9
September 2016
ICTIR '16: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 13, Downloads (12 Months): 151, Downloads (Overall): 151
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Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language ...
Keywords:
language model, paragraph vector
10
July 2016
IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability ...
11
July 2016
IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between ...
12
July 2016
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 14, Downloads (12 Months): 224, Downloads (Overall): 224
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Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic ...
Keywords:
neural tensor network, search result diversification, relational learning to rank
13
July 2016
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 15, Downloads (12 Months): 168, Downloads (Overall): 168
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In recent years, deep neural networks have yielded significant performance improvements on speech recognition and computer vision tasks, as well as led to exciting breakthroughs in novel application areas such as automatic voice translation, image captioning, and conversational agents. Despite demonstrating good performance on natural language processing (NLP) tasks (e.g., ...
Keywords:
deep learning, information retrieval, neural networks
14
July 2016
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 9, Downloads (12 Months): 258, Downloads (Overall): 258
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Incorporating topic level estimation into language models has been shown to be beneficial for information retrieval (IR) models such as cluster-based retrieval and LDA-based document representation. Neural embedding models, such as paragraph vector (PV) models, on the other hand have shown their effectiveness and efficiency in learning semantic representations of ...
Keywords:
language model, paragraph vector, retrieval model
15
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we ...
16
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching a question to its best answer is a common task in community question answering. In this paper, we focus on the non-factoid questions and aim to pick out the best answer from its candidate answers. Most of the existing deep models directly measure the similarity between question and answer ...
17
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its external contexts. However, distributional models cannot handle rare and morphologically complex words very well and fail to identify some fine-grained linguistic regularity as they are ignoring the word forms. On the contrary, ...
18
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns ...
19
February 2016
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 16, Downloads (12 Months): 150, Downloads (Overall): 334
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Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of ...
Keywords:
demographic attribute, multitask multi-class prediction, structured neural embedding
20
November 2015
Neurocomputing: Volume 167 Issue C, November 2015
Publisher: Elsevier Science Publishers B. V.
Query recommendation technology is of great importance for search engines, because it can assist users to find the information they require. Many query recommendation algorithms have been proposed, but they all aim to recommend similar queries and cannot guarantee the usefulness of the recommended queries. In this paper, we argue ...
Keywords:
Query ranking, Query log analysis, Query recommendation, Recommendation methods
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