2,940 research outputs found
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
Boosting Named Entity Recognition with Neural Character Embeddings
Most state-of-the-art named entity recognition (NER) systems rely on
handcrafted features and on the output of other NLP tasks such as
part-of-speech (POS) tagging and text chunking. In this work we propose a
language-independent NER system that uses automatically learned features only.
Our approach is based on the CharWNN deep neural network, which uses word-level
and character-level representations (embeddings) to perform sequential
classification. We perform an extensive number of experiments using two
annotated corpora in two different languages: HAREM I corpus, which contains
texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in
Spanish. Our experimental results shade light on the contribution of neural
character embeddings for NER. Moreover, we demonstrate that the same neural
network which has been successfully applied to POS tagging can also achieve
state-of-the-art results for language-independet NER, using the same
hyperparameters, and without any handcrafted features. For the HAREM I corpus,
CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score
for the total scenario (ten NE classes), and by 7.2 points in the F1 for the
selective scenario (five NE classes).Comment: 9 page
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
The recent tremendous success of unsupervised word embeddings in a multitude
of applications raises the obvious question if similar methods could be derived
to improve embeddings (i.e. semantic representations) of word sequences as
well. We present a simple but efficient unsupervised objective to train
distributed representations of sentences. Our method outperforms the
state-of-the-art unsupervised models on most benchmark tasks, highlighting the
robustness of the produced general-purpose sentence embeddings.Comment: NAACL 201
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