6,494 research outputs found
Relevance-based Word Embedding
Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR
Conference on Research and Development in Information Retrieval (SIGIR '17
End-to-end Learning for Short Text Expansion
Effectively making sense of short texts is a critical task for many real
world applications such as search engines, social media services, and
recommender systems. The task is particularly challenging as a short text
contains very sparse information, often too sparse for a machine learning
algorithm to pick up useful signals. A common practice for analyzing short text
is to first expand it with external information, which is usually harvested
from a large collection of longer texts. In literature, short text expansion
has been done with all kinds of heuristics. We propose an end-to-end solution
that automatically learns how to expand short text to optimize a given learning
task. A novel deep memory network is proposed to automatically find relevant
information from a collection of longer documents and reformulate the short
text through a gating mechanism. Using short text classification as a
demonstrating task, we show that the deep memory network significantly
outperforms classical text expansion methods with comprehensive experiments on
real world data sets.Comment: KDD'201
情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索
Tohoku University徳山豪課
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
A self-training approach for short text clustering
Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations for short texts. Low-dimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploited in deep clustering algorithms. While deep clustering has been studied extensively in computer vision, relatively little work has focused on NLP. The method we propose, learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network. Experiments on three short text datasets empirically validate the effectiveness of our method
A Broad Evaluation of the Tor English Content Ecosystem
Tor is among most well-known dark net in the world. It has noble uses,
including as a platform for free speech and information dissemination under the
guise of true anonymity, but may be culturally better known as a conduit for
criminal activity and as a platform to market illicit goods and data. Past
studies on the content of Tor support this notion, but were carried out by
targeting popular domains likely to contain illicit content. A survey of past
studies may thus not yield a complete evaluation of the content and use of Tor.
This work addresses this gap by presenting a broad evaluation of the content of
the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web
and, through topic and network analysis, characterize the types of information
and services hosted across a broad swath of Tor domains and their hyperlink
relational structure. We recover nine domain types defined by the information
or service they host and, among other findings, unveil how some types of
domains intentionally silo themselves from the rest of Tor. We also present
measurements that (regrettably) suggest how marketplaces of illegal drugs and
services do emerge as the dominant type of Tor domain. Our study is the product
of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a
collection of over 150,000 Tor pages. We make a dataset of the intend to make
the domain structure publicly available as a dataset at
https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page
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