Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

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From frequency to meaning: vector space models of semantics

The validity of the cubic law for laminar flow of fluids through open fractures consisting of parallel planar plates has been established by others over a wide range of conditions with apertures ranging down to a minimum of 0.2 µm. The law may be given in simplified form by Q/Δh = C(2b)3, where Q is the flow rate, Δh is the difference in hydraulic head, C is a constant that depends on the flow geometry and fluid properties, and 2b is the fracture aperture. The validity of this law for flow in a closed fracture where the surfaces are in contact and the aperture is being decreased under stress has been investigated at room temperature by using homogeneous samples of granite, basalt, and marble. Tension fractures were artificially induced, and the laboratory setup used radial as well as straight flow geometries. Apertures ranged from 250 down to 4µm, which was the minimum size that could be attained under a normal stress of 20 MPa. The cubic law was found to be valid whether the fracture surfaces were held open or were being closed under stress, and the results are not dependent on rock type. Permeability was uniquely defined by fracture aperture and was independent of the stress history used in these investigations. The effects of deviations from the ideal parallel plate concept only cause an apparent reduction in flow and may be incorporated into the cubic law by replacing C by C/ƒ. The factor ƒ varied from 1.04 to 1.65 in these investigations. The model of a fracture that is being closed under normal stress is visualized as being controlled by the strength of the asperities that are in contact. These contact areas are able to withstand significant stresses while maintaining space for fluids to continue to flow as the fracture aperture decreases. The controlling factor is the magnitude of the aperture, and since flow depends on (2b)3, a slight change in aperture evidently can easily dominate any other change in the geometry of the flow field. Thus one does not see any noticeable shift in the correlations of our experimental results in passing from a condition where the fracture surfaces were held open to one where the surfaces were being closed under stress.

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VALIDITY OF CUBIC LAW FOR FLUID FLOW IN A DEFORMABLE ROCK FRACTURE - eScholarship

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.

/pdf/a-hierarchical-recurrent-encoder-decoder-for-generative-2eq6r7hw9p.pdf

A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion

Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.

https://dl.acm.org/doi/pdf/10.1145/3109859.3109872

When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

User behavior provides many cues to improve the relevance of search results through personalization One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone We also characterize how the relative contribution of each model changes throughout the duration of a session Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience

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Modeling the impact of short- and long-term behavior on search personalization

Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.

/pdf/context-aware-query-suggestion-by-mining-click-through-and-4xnbx5op43.pdf

Context-aware query suggestion by mining click-through and session data

We propose an ultra-wideband plasmonic waveguide based on designer surface plasmon polaritons (DSPPs) with double gratings. In such plasmonic metamaterials, the DSPP waves in the region of lower frequencies of the dispersion curve can be tightly confined and hence effectively broaden the operating bandwidth. Based on such features, we design and fabricate a high performance DSPP filter, in which a transducer consisting of microstrip, slotline, and gradient corrugations is employed to feed electromagnetic energies into the plasmonic waveguide with high efficiency. The simulated and measured results on reflection and transmission coefficients in the microwave frequency demonstrate the excellent filtering characteristics such as low loss, wide band, and high square ratio. The high performance DSPP waveguide and filter pave a way to develop advanced plasmonic integrated functional devices and circuits in the microwave and terahertz frequencies.

An ultra-wideband surface plasmonic filter in microwave frequency

Based on the dispersion relation, surface plasmon polaritons (SPPs) or spoof SPPs are always propagating surface waves when the operating frequency is below the asymptotic limit - the surface plasma frequency. Here we propose a method to control the rejections of spoof SPPs using metamaterial particles. By introducing electrically resonant metamaterials near an ultrathin corrugated metallic strip - the spoof SPP waveguide - to produce tight coupling and mismatch of surface impedance, we show that the SPP modes are rejected near the resonant frequencies within the propagating band. Through the modulation of scaling factor of metamaterial particles, we can manipulate the rejections of SPP modes from narrowband to broadband. Both simulation and experiment results verify the tunability of SPP rejections, which have important applications in filtering SPP waves in plasmonic circuits and systems.

Controlling rejections of spoof surface plasmon polaritons using metamaterial particles

We propose a broadband and high-efficiency transition from a microstrip line to a conformal surface plasmon (CSP) waveguide that is made of an ultrathin corrugated metallic strip, to transform the guide wave into a spoof surface plasmon polariton (SPP) in the microwave region. The transition consists of three parts: a convertor which converts the direction of the electric field from perpendicular to parallel to the strip, a matching area with gradient corrugations and a flaring metallic line to match both the momentum and impedance, and a CSP waveguide to support the SPP waves. A back-to-back transition sample is fabricated using the proposed method. Experimental results of S parameters and near-field distributions verify the excellent performance of the sample to transform guided waves to SPPs and transmit SPP waves in a wide band. The sample exhibits low energy loss when the CSP waveguide is bent or even twisted. The proposed transition may have potential applications in integrating conventional microwave devices with the SPP devices.

https://iopscience.iop.org/article/10.1088/0022-3727/47/31/315103/pdf

Broadband transition between microstrip line and conformal surface plasmon waveguide

Web query recommendation has long been considered a key feature of search engines. Building a good Web query recommendation system, however, is very difficult due to the fundamental challenge of predicting users' search intent, especially given the limited user context information. In this paper, we propose a novel "sequential query prediction" approach that tries to grasp a user's search intent based on his/her past query sequence and its resemblance to historical query sequence models mined from massive search engine logs. Different query sequence models were examined, including the naive variable length N-gram model, Variable Memory Markov (VMM) model, and our proposed Mixture Variable Memory Markov (MVMM) model. Extensive experiments were conducted to benchmark our sequence prediction algorithms against two conventional pairwise approaches on large-scale search logs extracted from a commercial search engine. Results show that the sequence-wise approaches significantly outperform the conventional pair-wise ones in terms of prediction accuracy. In particular, our MVMM approach, consistently leads the pack, making it an effective and practical approach towards Web query recommendation.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1327&context=sis_research

Zhen Liao

Papers

Context-aware query suggestion by mining click-through and session data

An ultra-wideband surface plasmonic filter in microwave frequency

Controlling rejections of spoof surface plasmon polaritons using metamaterial particles

Broadband transition between microstrip line and conformal surface plasmon waveguide

Web Query Recommendation via Sequential Query Prediction