15,235 research outputs found
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
In this paper we propose a general framework for learning distributed
representations of attributes: characteristics of text whose representations
can be jointly learned with word embeddings. Attributes can correspond to
document indicators (to learn sentence vectors), language indicators (to learn
distributed language representations), meta-data and side information (such as
the age, gender and industry of a blogger) or representations of authors. We
describe a third-order model where word context and attribute vectors interact
multiplicatively to predict the next word in a sequence. This leads to the
notion of conditional word similarity: how meanings of words change when
conditioned on different attributes. We perform several experimental tasks
including sentiment classification, cross-lingual document classification, and
blog authorship attribution. We also qualitatively evaluate conditional word
neighbours and attribute-conditioned text generation.Comment: 11 pages. An earlier version was accepted to the ICML-2014 Workshop
on Knowledge-Powered Deep Learning for Text Minin
Enhancing Domain Word Embedding via Latent Semantic Imputation
We present a novel method named Latent Semantic Imputation (LSI) to transfer
external knowledge into semantic space for enhancing word embedding. The method
integrates graph theory to extract the latent manifold structure of the
entities in the affinity space and leverages non-negative least squares with
standard simplex constraints and power iteration method to derive spectral
embeddings. It provides an effective and efficient approach to combining entity
representations defined in different Euclidean spaces. Specifically, our
approach generates and imputes reliable embedding vectors for low-frequency
words in the semantic space and benefits downstream language tasks that depend
on word embedding. We conduct comprehensive experiments on a carefully designed
classification problem and language modeling and demonstrate the superiority of
the enhanced embedding via LSI over several well-known benchmark embeddings. We
also confirm the consistency of the results under different parameter settings
of our method.Comment: ACM SIGKDD 201
Toxic comment classification using convolutional and recurrent neural networks
This thesis aims to provide a reasonable solution for categorizing automatically sentences into types of toxicity using different types of neural networks. There are six types of categories: Toxic, severe toxic, obscene, threat, insult and identity hate. Three different implementations have been studied to accomplish the objective: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and convolutional neural networks. The thesis is not thought to aim on improving the performance of every individual model but on the comparison between them in terms of natural language processing adequacy. In addition, one differential aspect about this project is the research of LSTM neurons activations and thus the relationship of the words with the final sentence classificatory decision. In conclusion, the three models performed almost equally and the extraction of LSTM activations provided a very accurate and visual understanding of the decisions taken by the network.Esta tesis tiene como objetivo aportar una buena solución para la categorización automática de comentarios abusivos haciendo uso de distintos tipos de redes neuronales. Hay seis categorías: Tóxico, muy tóxico, obsceno, insulto, amenaza y racismo. Se ha hecho una investigación de tres implementaciones para llevar a cabo el objetivo: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) y redes convolucionales. El objetivo de este trabajo no es intentar mejorar al máximo el resultado de la clasificación sino hacer una comparación de los 3 modelos para los mismos parámetros e intentar saber cuál funciona mejor para este caso de procesado de lenguaje. Además, un aspecto diferencial de este proyecto es la investigación sobre las activaciones de las neuronas en el modelo LSTM y su relación con la importancia de las palabras respecto a la clasificación final de la frase. En conclusión, los tres modelos han funcionado de forma casi idéntica y la extracción de las activaciones han proporcionado un conocimiento muy preciso y visual de las decisiones tomadas por la red.Aquesta tesi té com a objectiu aportar una bona solució per categoritzar automàticament comentaris abusius usant diferents tipus de xarxes neuronals. Hi ha sis tipus de categories: Tòxic, molt tòxic, obscè, insult, amenaça i racisme. S'ha fet una recerca de tres implementacions per dur a terme l'objectiu: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) i xarxes convolucionals. L'objectiu d'aquest treball no és intentar millorar al màxim els resultats de classificació sinó fer una comparació dels 3 models pels mateixos paràmetres per tal d'esbrinar quin funciona millor en aquest cas de processat de llenguatge. A més, un aspecte diferencial d'aquest projecte és la recerca sobre les activacions de les neurones al model LSTM i la seva relació amb la importància de les paraules respecte la classificació final de la frase. En conclusió, els tres models han funcionat gairebé idènticament i l'extracció de les activacions van proporcionar un enteniment molt acurat i visual de les decisions preses per la xarxa
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