10,889 research outputs found
Deep Short Text Classification with Knowledge Powered Attention
Short text classification is one of important tasks in Natural Language
Processing (NLP). Unlike paragraphs or documents, short texts are more
ambiguous since they have not enough contextual information, which poses a
great challenge for classification. In this paper, we retrieve knowledge from
external knowledge source to enhance the semantic representation of short
texts. We take conceptual information as a kind of knowledge and incorporate it
into deep neural networks. For the purpose of measuring the importance of
knowledge, we introduce attention mechanisms and propose deep Short Text
Classification with Knowledge powered Attention (STCKA). We utilize Concept
towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)
attention to acquire the weight of concepts from two aspects. And we classify a
short text with the help of conceptual information. Unlike traditional
approaches, our model acts like a human being who has intrinsic ability to make
decisions based on observation (i.e., training data for machines) and pays more
attention to important knowledge. We also conduct extensive experiments on four
public datasets for different tasks. The experimental results and case studies
show that our model outperforms the state-of-the-art methods, justifying the
effectiveness of knowledge powered attention
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
Neural network techniques are widely applied to obtain high-quality
distributed representations of words, i.e., word embeddings, to address text
mining, information retrieval, and natural language processing tasks. Recently,
efficient methods have been proposed to learn word embeddings from context that
captures both semantic and syntactic relationships between words. However, it
is challenging to handle unseen words or rare words with insufficient context.
In this paper, inspired by the study on word recognition process in cognitive
psychology, we propose to take advantage of seemingly less obvious but
essentially important morphological knowledge to address these challenges. In
particular, we introduce a novel neural network architecture called KNET that
leverages both contextual information and morphological word similarity built
based on morphological knowledge to learn word embeddings. Meanwhile, the
learning architecture is also able to refine the pre-defined morphological
knowledge and obtain more accurate word similarity. Experiments on an
analogical reasoning task and a word similarity task both demonstrate that the
proposed KNET framework can greatly enhance the effectiveness of word
embeddings
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Intelligence Quotient (IQ) Test is a set of standardized questions designed
to evaluate human intelligence. Verbal comprehension questions appear very
frequently in IQ tests, which measure human's verbal ability including the
understanding of the words with multiple senses, the synonyms and antonyms, and
the analogies among words. In this work, we explore whether such tests can be
solved automatically by artificial intelligence technologies, especially the
deep learning technologies that are recently developed and successfully applied
in a number of fields. However, we found that the task was quite challenging,
and simply applying existing technologies (e.g., word embedding) could not
achieve a good performance, mainly due to the multiple senses of words and the
complex relations among words. To tackle these challenges, we propose a novel
framework consisting of three components. First, we build a classifier to
recognize the specific type of a verbal question (e.g., analogy,
classification, synonym, or antonym). Second, we obtain distributed
representations of words and relations by leveraging a novel word embedding
method that considers the multi-sense nature of words and the relational
knowledge among words (or their senses) contained in dictionaries. Third, for
each type of questions, we propose a specific solver based on the obtained
distributed word representations and relation representations. Experimental
results have shown that the proposed framework can not only outperform existing
methods for solving verbal comprehension questions but also exceed the average
performance of the Amazon Mechanical Turk workers involved in the study. The
results indicate that with appropriate uses of the deep learning technologies
we might be a further step closer to the human intelligence
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
User interaction with voice-powered agents generates large amounts of
unlabeled utterances. In this paper, we explore techniques to efficiently
transfer the knowledge from these unlabeled utterances to improve model
performance on Spoken Language Understanding (SLU) tasks. We use Embeddings
from Language Model (ELMo) to take advantage of unlabeled data by learning
contextualized word representations. Additionally, we propose ELMo-Light
(ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our
findings suggest unsupervised pre-training on a large corpora of unlabeled
utterances leads to significantly better SLU performance compared to training
from scratch and it can even outperform conventional supervised transfer.
Additionally, we show that the gains from unsupervised transfer techniques can
be further improved by supervised transfer. The improvements are more
pronounced in low resource settings and when using only 1000 labeled in-domain
samples, our techniques match the performance of training from scratch on
10-15x more labeled in-domain data.Comment: To appear at AAAI 201
Programming Bots by Synthesizing Natural Language Expressions into API Invocations
At present, bots are still in their preliminary stages of development. Many
are relatively simple, or developed ad-hoc for a very specific use-case. For
this reason, they are typically programmed manually, or utilize
machine-learning classifiers to interpret a fixed set of user utterances. In
reality, real world conversations with humans require support for dynamically
capturing users expressions. Moreover, bots will derive immeasurable value by
programming them to invoke APIs for their results. Today, within the Web and
Mobile development community, complex applications are being stringed together
with a few lines of code -- all made possible by APIs. Yet, developers today
are not as empowered to program bots in much the same way. To overcome this, we
introduce BotBase, a bot programming platform that dynamically synthesizes
natural language user expressions into API invocations. Our solution is two
faceted: Firstly, we construct an API knowledge graph to encode and evolve
APIs; secondly, leveraging the above we apply techniques in NLP, ML and Entity
Recognition to perform the required synthesis from natural language user
expressions into API calls.Comment: The paper is published at ASE 2017 (The 32nd IEEE/ACM International
Conference on Automated Software Engineering
Which Emoji Talks Best for My Picture?
Emojis have evolved as complementary sources for expressing emotion in
social-media platforms where posts are mostly composed of texts and images. In
order to increase the expressiveness of the social media posts, users associate
relevant emojis with their posts. Incorporating domain knowledge has improved
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emoji recommendation task in
case of multimedia posts composed of image and text. Our emoji recommendation
can suggest accurate emojis by exploiting both visual and textual content from
social media posts as well as domain knowledge from Emojinet. Experimental
results using pre-trained image classifiers and pre-trained word embedding
models on Twitter dataset show that our results outperform the current
state-of-the-art by 9.6\%. We also present a user study evaluation of our
recommendation system on a set of images chosen from MSCOCO dataset.Comment: Accepted at the 2018 IEEE/WIC/ACM International Conference on Web
Intelligence (WI '18), December 3-6, 2018, Santiago de Chil
WordRep: A Benchmark for Research on Learning Word Representations
WordRep is a benchmark collection for the research on learning distributed
word representations (or word embeddings), released by Microsoft Research. In
this paper, we describe the details of the WordRep collection and show how to
use it in different types of machine learning research related to word
embedding. Specifically, we describe how the evaluation tasks in WordRep are
selected, how the data are sampled, and how the evaluation tool is built. We
then compare several state-of-the-art word representations on WordRep, report
their evaluation performance, and make discussions on the results. After that,
we discuss new potential research topics that can be supported by WordRep, in
addition to algorithm comparison. We hope that this paper can help people gain
deeper understanding of WordRep, and enable more interesting research on
learning distributed word representations and related topics
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
We present a new perspective on neural knowledge base (KB) embeddings, from
which we build a framework that can model symbolic knowledge in the KB together
with its learning process. We show that this framework well regularizes
previous neural KB embedding model for superior performance in reasoning tasks,
while having the capabilities of dealing with unseen entities, that is, to
learn their embeddings from natural language descriptions, which is very like
human's behavior of learning semantic concepts.Comment: Accepted to NIPS 2015 Cognitive Computation workshop (CoCo@NIPS 2015
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
How can we enable computers to automatically answer questions like "Who
created the character Harry Potter"? Carefully built knowledge bases provide
rich sources of facts. However, it remains a challenge to answer factoid
questions raised in natural language due to numerous expressions of one
question. In particular, we focus on the most common questions --- ones that
can be answered with a single fact in the knowledge base. We propose CFO, a
Conditional Focused neural-network-based approach to answering factoid
questions with knowledge bases. Our approach first zooms in a question to find
more probable candidate subject mentions, and infers the final answers with a
unified conditional probabilistic framework. Powered by deep recurrent neural
networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7%
on a dataset of 108k questions - the largest public one to date. It outperforms
the current state of the art by an absolute margin of 11.8%.Comment: Accepted by ACL 201
SubGram: Extending Skip-gram Word Representation with Substrings
Skip-gram (word2vec) is a recent method for creating vector representations
of words ("distributed word representations") using a neural network. The
representation gained popularity in various areas of natural language
processing, because it seems to capture syntactic and semantic information
about words without any explicit supervision in this respect. We propose
SubGram, a refinement of the Skip-gram model to consider also the word
structure during the training process, achieving large gains on the Skip-gram
original test set.Comment: Published at TSD 201
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