57,444 research outputs found
Evaluating neural multi-field document representations for patent classification
Patent classification constitutes a long-tailed hierarchical learning problem. Prior work has demonstrated the efficacy of neural representations based on pre-trained transformers, however, due to the limited input size of these models, using only title and abstract of patents as input. Patent documents consist of several textual fields, some of which are quite long. We show that a baseline using simple tf.idf-based methods can easily leverage this additional information. We propose a new architecture combining the neural transformer-based representations of the various fields into a meta-embedding, which we demonstrate to outperform the tf.idf-based counterparts especially on less frequent classes. Using a relatively simple architecture, we outperform the previous state of the art on CPC classification by a margin of 1.2 macro-avg. F1 and 2.6 micro-avg. F1. We identify the textual field giving a “brief-summary” of the patent as most informative with regard to CPC classification, which points to interesting future directions of research on less computation-intensive models, e.g., by summarizing long documents before neural classification
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Structural Regularities in Text-based Entity Vector Spaces
Entity retrieval is the task of finding entities such as people or products
in response to a query, based solely on the textual documents they are
associated with. Recent semantic entity retrieval algorithms represent queries
and experts in finite-dimensional vector spaces, where both are constructed
from text sequences.
We investigate entity vector spaces and the degree to which they capture
structural regularities. Such vector spaces are constructed in an unsupervised
manner without explicit information about structural aspects. For concreteness,
we address these questions for a specific type of entity: experts in the
context of expert finding. We discover how clusterings of experts correspond to
committees in organizations, the ability of expert representations to encode
the co-author graph, and the degree to which they encode academic rank. We
compare latent, continuous representations created using methods based on
distributional semantics (LSI), topic models (LDA) and neural networks
(word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as
doc2vec and SERT, systematically perform better at clustering than LSI, LDA and
word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the
Theory of Information Retrieval. 201
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