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Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil

In this paper, we propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents. In order to capture the rich contextual structures in a query or a document, we start with each word within a temporal context window in a word sequence to directly capture contextual features at the word n-gram level. Next, the salient word n-gram features in the word sequence are...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: CIKM
Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi

Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query.

Perhaps surprisingly, despite QAC being widely used, users’ interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
, , jibian, bingao, , and tyliu

Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since text...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: Choose...
Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen

This paper presents a deep semantic similarity model (DSSM) for recommending target documents to be of interest to a user based on a source document she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent...

Publication details
Date: 1 October 2014
Type: Technical report
Publisher: EMNLP
Number: MSR-TR-2014-56
Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen

This paper presents a deep semantic model (DSM) for recommending target documents to be of interest to a user based on a source document she is reading. We observe, identify, and detect naturally occurring signals of interestingness in click transitions on the Web between source and target documents, which we collect from commercial Web browser logs. The DSM is trained on millions of Web transitions, and maps source-target document pairs to feature vectors in a latent space in such a...

Publication details
Date: 1 October 2014
Type: Proceedings
Publisher: EMNLP
Michael Auli, Michel Galley, and Jianfeng Gao

Recent work by Cherry (2013) has shown that directly optimizing phrase-based reordering models towards BLEU can lead to significant gains. Their approach is limited to small training sets of a few thousand sentences and a similar number of sparse features. We show how the expected BLEU objective allows us to train a simple linear discriminative reordering model with millions of sparse features on hundreds of thousands of sentences resulting in significant improvements. A comparison to likelihood...

Publication details
Date: 1 October 2014
Type: Proceedings
Publisher: EMNLP
Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek

While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowledge bases and textual data, the performance of relation extraction can be improved significantly. Following this new paradigm, we propose a tensor decomposition approach for knowledge base embedding that is highly scalable, and is especially suitable for relation extraction. By leveraging...

Publication details
Date: 1 October 2014
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Mohammed Shoaib, Jie Liu, and Matthai Phillipose

High functional complexity is leading us towards new architectures for sensing systems. Multi-tiered design is one among the many emerging alternatives. Such architectures bring new opportunities for effective system-level power management. For instance, varying one/more tier-level parameters can provide substantial end-to-end energy scaling. In this paper, we review an existing approach that shows how one such parameter, namely data compression, can help us scale energy at the cost of algorithmic...

Publication details
Date: 14 September 2014
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Li Deng and John C. Platt

Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-linear stacking methods for ensemble learning with applications specifically to speechclass posterior...

Publication details
Date: 1 September 2014
Type: Inproceeding
Publisher: Proc. Interspeech
Alex Marin, Roman Holenstein, Ruhi Sarikaya, and Mari Ostendorf

This paper explores a novel method for learning phrase pattern features for text classification, employing a mapping of selected words into a knowledge graph and self-training over unlabeled data. Using Support Vector Machine classification, we obtain improvements over lexical and fully-supervised phrase pattern features in domain and intent detection for language understanding, particularly in conjunction with the use of unlabeled data. Our best results are obtained using unlabeled data filtered for...

Publication details
Date: 1 September 2014
Type: Proceedings
Publisher: ISCA - International Speech Communication Association
Jean-Philippe Robichaud, Paul A. Crook, Puyang Xu, Omar Zia Khan, and Ruhi Sarikaya

We present a novel application of hypothesis ranking (HR) for the task of domain detection in a multi-domain, multiturn dialog system. Alternate, domain dependent, semantic frames from a spoken language understanding (SLU) analysis are ranked using a gradient boosted decision trees (GBDT) ranker to determine the most likely domain. The ranker, trained using Lambda Rank, makes use of a range of signals derived from the SLU and previous turn context to improve domain detection. On a multi-turn corpus we...

Publication details
Date: 1 September 2014
Type: Inproceeding
Publisher: ISCA - International Speech Communication Association
Jiang Bian, Bin Gao, and Tie-Yan Liu

Recent years have witnessed the increasing efforts that apply deep learning techniques to solve text mining and natural language processing tasks. The basis of these tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually contains limited information, which makes necessity to leverage extra knowledge to understand it. Fortunately, since text is generated by human, it already contains well-defined...

Publication details
Date: 1 September 2014
Type: Inproceeding
Publisher: Springer
Puyang Xu and Ruhi Sarikaya

In slot filling with conditional random field (CRF), the strong current word and dictionary features tend to swamp the effect of contextual features, a phenomenon also known as feature undertraining. This is a dangerous tradeoff especially when training data is small and dictionaries are limited in its coverage of the entities observed during testing. In this paper, we propose a simple and effective solution that extends the feature dropout algorithm, directly aiming at boosting the...

Publication details
Date: 1 September 2014
Type: Proceedings
Publisher: ISCA - International Speech Communication Association
Publication details
Date: 1 August 2014
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2014-109
Nihar B. Shah and Dengyong Zhou

Many fields of science and engineering, ranging from predicting protein structures to building machine translation systems, require large amounts of labeled data. These labeling tasks have traditionally been performed by experts; the limited pool of experts would limit the size of the datasets, and make the process slow and expensive. In recent years, there is a rapidly increasing interest in using crowds of semi-skilled workers recruited through the Internet. While this 'crowdsourcing' can cheaply...

Publication details
Date: 1 August 2014
Type: Technical report
Publisher: Choose...
Number: MSR-TR-2014-117
Yanjie Fu, Hui Xiong, Yong Ge, Zijun Yao, Yu Zheng, and Zhi-Hua Zhou

It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed,...

Publication details
Date: 1 August 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, and Tie-Yan Liu

The techniques of using neural networks to learn distributed word representations (i.e., word embeddings) have been used to solve a variety of natural language processing tasks. The recently proposed methods, such as CBOW and Skip-gram, have demonstrated their effectiveness in learning word embeddings based on context information such that the obtained word embeddings can capture both semantic and syntactic relationships between words. However, it is quite challenging to produce high-quality word...

Publication details
Date: 1 August 2014
Type: Inproceeding
Publisher: Choose...
Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, and Tie-Yan Liu

Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim at generating only one embedding vector for each individual word, which, however, limits their effectiveness because huge amounts of words are polysemous (such as \emph{bank} and \emph{star}). To address this problem, it is necessary to build multi embedding vectors to represent different meanings of a word...

Publication details
Date: 1 August 2014
Type: Inproceeding
Publisher: Choose...
David Heckerman, Christopher Meek, and Thomas S. Richardson

We compare alternative definitions of undirected graphical models for discrete, finite variables. Lauritzen (1996) provides several definitions of such models and describes their relationships. He shows that the definitions agree only when joint distributions represented by the models are limited to strictly positive distributions. Heckerman et al. (2000), in their paper on dependency networks, describe another definition of undirected graphical models for strictly positive distributions. They show that...

Publication details
Date: 1 July 2014
Type: Article
Publisher: Kybernetika
Yang Song, Xiaolin Shi, Ryen White, and Ahmed Hassan
Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: ACM
Matthai Philipose

We examine how to use emerging far-infrared imager ensembles to detect certain objects of interest (e.g., faces, hands, people and animals) in synchronized RGB video streams at very low power. We formulate the problem as one of selecting subsets of sensing elements (among many thousand possibilities) from the ensembles for tests. The subset selection problem is naturally adaptive and online : testing certain elements early can obviate the need for testing many others later, and...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Hamid Palangi, Li Deng, and Rabab K Ward

Deep Stacking Networks (DSNs) are constructed by stacking shallow feed-forward neural networks on top of each other using concatenated features derived from the lower modules of the DSN and the raw input data. DSNs do not have recurrent connections, making them less effective to model and classify input data with temporal dependencies. In this paper, we embed recurrent connections into the DSN, giving rise to Recurrent Deep Stacking Networks (R-DSNs). Each module of the R-DSN consists of a special form...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: IEEE Conference ChinaSIP
Long Tran-Thanh, Lampros Stavrogiannis, Victor Naroditskiy, Valentin Robu, Nicholas R Jennings, and Peter Key

We study the problem of an advertising agent who needs to intelligently distribute her budget across a sequence of online keyword bidding auctions. We assume the closing price of each auction is governed by the same unknown distribution, and study the problem of making provably optimal bidding decisions. Learning the distribution is done under censored observations, i.e. the closing price of an auction is revealed only if the bid we place is above it. We consider three algorithms, namely...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: AUAI
Tomáš Kocák, Michal Valko, Rémi Munos, and Shipra Agrawal
Publication details
Date: 1 July 2014
Type: Article
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Sean Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek

We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of humancomputer interaction and capture scenarios....

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
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