Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

Abstract

User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. And, unfortunately, unsupervised topic models often fail to capture the aspects of interest. In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest. First, we show that current weakly supervised approaches do not effectively leverage the predictive power of seed words for aspect detection. Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings). Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models. Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews.

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

“…As illustrated in Figure 3, translated seed words (e.g., "parfait") often co-occur with other words (e.g., "aime," meaning "love") that have zero weight inẐ but are also helpful for the task at hand. To exploit such words in the absence of labeled target documents, we extend the monolingual weaklysupervised co-training method of Karamanolakis et al (2019) to our cross-lingual setting, and use our classifier based on translated seed words as a teacher to train a student, as we describe next. First, CLTS uses our classifier from Equation 5 as a teacher to predict labels q j for unlabeled documents x T j ∈ D T that contain seed words:…”

“…Note that our teacher with weights transferred across languages is different than that of Karamanolakis et al (2019), which simply "counts" seed words.…”

“…larger training set for the student. This also differs from Karamanolakis et al (2019), which updates the weights of the initial seed words but does not provide pseudo-labels for documents with no seed words. This change is important in our crosslingual setting with a limited translation budget, where the translated seed words G T may only cover a very small subset D T of D T .…”

“…Because translations can induce errors and the translation budget is limited, the translated seed words may be noisy and not comprehensive for the task at hand. As a second contribution, we extend the "weakly-supervised co-training" method of Karamanolakis et al (2019) to our cross-lingual setting for training a stronger student classifier using the teacher and unlabeled-only target documents. The student outperforms the teacher across all languages by 59.6%.…”

An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning

“…The four most commonly used algorithms for semi-supervised learning are Self-Training [17], Co-Training [18], Generative Model [19] and Graph-Based Semi-supervised [20]. The Self-Training algorithm refers to the use of a self-classifier to continuously generate high-confidence samples for improving the final classification performance.…”