Learning to Embed Words in Context for Syntactic Tasks

We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho

Trans-gram, Fast Cross-lingual Word-embeddings

We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.Comment: EMNLP 201

Spam Detection Using Machine Learning and Deep Learning

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove these spam messages is important. This dissertation explores the process of text classification from data input to embedded representation of the words in vector form and finally the classification process. Therefore, we have applied different embedding methods to capture both the linguistic and semantic meanings of words. Static embedding methods that are used include Word to Vector (Word2Vec) and Global Vectors (GloVe), while for dynamic embedding the transfer learning of the Bidirectional Encoder Representations from Transformers (BERT) was employed. For classification, both machine learning and deep learning techniques were used to build an efficient and sensitive classification model with good accuracy and low false positive rate. Our result established that the combination of BERT for embedding and machine learning for classification produced better classification results than other combinations. With these results, we developed models that combined the self-feature extraction advantage of deep learning and the effective classification of machine learning. These models were tested on four different datasets, namely: SMS Spam dataset, Ling dataset, Spam Assassin dataset and Enron dataset. BERT+SVC (hybrid model) produced the result with highest accuracy and lowest false positive rate