83,955 research outputs found
Neural Ranking Models with Weak Supervision
Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents when no supervised signal is available. Hence, in this paper, we
propose to train a neural ranking model using weak supervision, where labels
are obtained automatically without human annotators or any external resources
(e.g., click data). To this aim, we use the output of an unsupervised ranking
model, such as BM25, as a weak supervision signal. We further train a set of
simple yet effective ranking models based on feed-forward neural networks. We
study their effectiveness under various learning scenarios (point-wise and
pair-wise models) and using different input representations (i.e., from
encoding query-document pairs into dense/sparse vectors to using word embedding
representation). We train our networks using tens of millions of training
instances and evaluate it on two standard collections: a homogeneous news
collection(Robust) and a heterogeneous large-scale web collection (ClueWeb).
Our experiments indicate that employing proper objective functions and letting
the networks to learn the input representation based on weakly supervised data
leads to impressive performance, with over 13% and 35% MAP improvements over
the BM25 model on the Robust and the ClueWeb collections. Our findings also
suggest that supervised neural ranking models can greatly benefit from
pre-training on large amounts of weakly labeled data that can be easily
obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR2017
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune.Comment: KDD 201
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
Intelligent search for distributed information sources using heterogeneous neural networks
As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources
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