Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as human-facing use cases. Existing supervised systems for keyphrase extraction require large amounts of labeled training data and generalize very poorly outside the domain of the training data. At the same time, unsupervised systems found in the literature have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Furthermore, both supervised and unsupervised methods are often too slow for real-time scenarios and suffer from over-generation. Addressing these drawbacks, in this paper, we introduce an unsupervised method for keyphrase extraction from single documents that leverages sentence embeddings. By selecting phrases whose semantic embeddings are close to the embeddings of the whole document, we are able to separate the best candidate phrases from the rest. We show that our embedding-based method is not only simpler, but also more effective than graph-based state of the art systems, achieving higher F-scores on standard datasets. Simplicity is a significant advantage, especially when processing large amounts of documents from the Web, resulting in considerable speed gains. Moreover, we describe how to increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrase semantic overlap leads to no gains in terms of F-score, our diversity enriched selection is preferred by humans.

Learning computationally efficient static word and sentence representations

Prakhar Gupta

Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, static/non-contextual and contextual. In a static embedding, the vector representation of a word is independent of its context as opposed to a contextual embedding where the word representation incorporates additional information from its surrounding context.Recently, advancements in deep learning when applied to contextual embeddings have seen them outperforming their static counterparts. However, this improvement in performance with respect to that of the static embeddings has come at the cost of lesser computational efficiency in terms of both computational resources as well as training and inference times, relative lack of interpretability, and higher costs to the environment. Consequently, static embedding models despite not being as expressive and powerful as contextual embedding models continue to be of relevance in Natural Language Processing Research.In this thesis, we propose improvements to the current state-of-the-art static word embedding and sentence embedding models in three different settings. Firstly, we propose an improved algorithm to learn word and sentence embedding from raw text by proposing changes to the Word2Vec training objective formulation and adding n-grams to the training to incorporate local contextual information. Consequently, we end up obtaining improved unsupervised static word and sentence embeddings. Our second major contribution is learning cross-lingual static word and sentence representations from parallel bilingual data where two corpora are aligned sentence-wise. Our word and sentence embeddings thus obtained outperform other bag-of-words bilingual embeddings on cross-lingual sentence retrieval and monolingual word similarity tasks while staying competitive with them on cross-lingual word translation tasks. In our last major contribution, we aim towards harnessing the expressive power of the contextual embedding models by distilling static word embeddings from contextual embedding models to use improved word representations for computationally light tasks. This allows us to utilize the semantic information possessed by the contextual embedding models while maintaining computational efficiency for inference tasks at the same time.

EPFL2021

Building Word Embeddings for Solving Natural Language Processing

Rémi Philippe Lebret

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for capturing both syntactic and semantic information about words. Word embeddings, when used as the underlying input representation, have been shown to be a great asset for a large variety of natural language processing (NLP) tasks. Recent techniques to obtain such word embeddings are mostly based on neural network language models (NNLM). In such systems, the word vectors are randomly initialized and then trained to predict optimally the contexts in which the corresponding words tend to appear. Because words occurring in similar contexts have, in general, similar meanings, their resulting word embeddings are semantically close after training. However, such architectures might be challenging and time-consuming to train. In this thesis, we are focusing on building simple models which are fast and efficient on large-scale datasets. As a result, we propose a model based on counts for computing word embeddings. A word co-occurrence probability matrix can easily be obtained by directly counting the context words surrounding the vocabulary words in a large corpus of texts. The computation can then be drastically simplified by performing a Hellinger PCA of this matrix. Besides being simple, fast and intuitive, this method has two other advantages over NNLM. It first provides a framework to infer unseen words or phrases. Secondly, all embedding dimensions can be obtained after a single Hellinger PCA, while a new training is required for each new size with NNLM. We evaluate our word embeddings on classical word tagging tasks and show that we reach similar performance than with neural network based word embeddings. While many techniques exist for computing word embeddings, vector space models for phrases remain a challenge. Still based on the idea of proposing simple and practical tools for NLP, we introduce a novel model that jointly learns word embeddings and their summation. Sequences of words (i.e. phrases) with different sizes are thus embedded in the same semantic space by just averaging word embeddings. In contrast to previous methods which reported a posteriori some compositionality aspects by simple summation, we simultaneously train words to sum, while keeping the maximum information from the original vectors. These word and phrase embeddings are then used in two different NLP tasks: document classification and sentence generation. Using such word embeddings as inputs, we show that good performance is achieved in sentiment classification of short and long text documents with a convolutional neural network. Finding good compact representations of text documents is crucial in classification systems. Based on the summation of word embeddings, we introduce a method to represent documents in a low-dimensional semantic space. This simple operation, along with a clustering method, provides an efficient framework for adding semantic information to documents, which yields better results than classical approaches for classification. Simple models for sentence generation can also be designed by leveraging such phrase embeddings. We propose a phrase-based model for image captioning which achieves similar results than those obtained with more complex models. Not only word and phrase embeddings but also embeddings for non-textual elements can be helpful for sentence generation. We, therefore, explore to embed table elements for generating better sentences from structured data. We experiment this approach with a large-scale dataset of biographies, where biographical infoboxes were available. By parameterizing both words and fields as vectors (embeddings), we significantly outperform a classical model.

École Polytechnique Fédérale de Lausanne2016

Open-Vocabulary Keyword Spotting with Audio and Text Embeddings

Milos Cernak, Martin Jaggi, Niccolò Sacchi

Keyword Spotting (KWS) systems allow detecting a set of spoken (pre-defined) keywords. Open-vocabulary KWS systems search for the keywords in the set of word hypotheses generated by an automatic speech recognition (ASR) system which is computationally expensive and, therefore, often implemented as a cloud-based service. Besides, KWS systems could use also word classification algorithms that do not allow easily changing the set of words to be recognized, as the classes have to be defined a priori, even before training the system. In this paper, we propose the implementation of an open-vocabulary ASR-free KWS system based on speech and text encoders that allow matching the computed embeddings in order to spot whether a keyword has been uttered. This approach would allow choosing the set of keywords a posteriori while requiring low computational power. The experiments, performed on two different datasets, show that our method is competitive with other state of the art KWS systems while allowing for a flexibility of configuration and being computationally efficient.