Tipping the scales: exploring the added value of deep semantic processing on readability prediction and sentiment analysis

Applications which make use of natural language processing (NLP) are said to benefit more from incorporating a rich model of text meaning than from a basic representation in the form of bag-of-words. This thesis set out to explore the added value of incorporating deep semantic information in two end-user applications that normally rely mostly on superficial and lexical information, viz. readability prediction and aspect-based sentiment analysis. For both applications we apply supervised machine learning techniques and focus on the incorporation of coreference and semantic role information. To this purpose, we adapted a Dutch coreference resolution system and developed a semantic role labeler for Dutch. We tested the cross-genre robustness of both systems and in a next phase retrained them on a large corpus comprising a variety of text genres. For the readability prediction task, we first built a general-purpose corpus consisting of a large variety of text genres which was then assessed on readability. Moreover, we proposed an assessment technique which has not previously been used in readability assessment, namely crowdsourcing, and revealed that crowdsourcing is a viable alternative to the more traditional assessment technique of having experts assign labels. We built the first state-of-the-art classification-based readability prediction system relying on a rich feature space of traditional, lexical, syntactic and shallow semantic features. Furthermore, we enriched this tool by introducing new features based on coreference resolution and semantic role labeling. We then explored the added value of incorporating this deep semantic information by performing two different rounds of experiments. In the first round these features were manually in- or excluded and in the second round joint optimization experiments were performed using a wrapper-based feature selection system based on genetic algorithms. In both setups, we investigated whether there was a difference in performance when these features were derived from gold standard information compared to when they were automatically generated, which allowed us to assess the true upper bound of incorporating this type of information. Our results revealed that readability classification definitely benefits from the incorporation of semantic information in the form of coreference and semantic role features. More precisely, we found that the best results for both tasks were achieved after jointly optimizing the hyperparameters and semantic features using genetic algorithms. Contrary to our expectations, we observed that our system achieved its best performance when relying on the automatically predicted deep semantic features. This is an interesting result, as our ultimate goal is to predict readability based exclusively on automatically-derived information sources. For the aspect-based sentiment analysis task, we developed the first Dutch end-to-end system. We therefore collected a corpus of Dutch restaurant reviews and annotated each review with aspect term expressions and polarity. For the creation of our system, we distinguished three individual subtasks: aspect term extraction, aspect category classification and aspect polarity classification. We then investigated the added value of our two semantic information layers in the second subtask of aspect category classification. In a first setup, we focussed on investigating the added value of performing coreference resolution prior to classification in order to derive which implicit aspect terms (anaphors) could be linked to which explicit aspect terms (antecedents). In these experiments, we explored how the performance of a baseline classifier relying on lexical information alone would benefit from additional semantic information in the form of lexical-semantic and semantic role features. We hypothesized that if coreference resolution was performed prior to classification, more of this semantic information could be derived, i.e. for the implicit aspect terms, which would result in a better performance. In this respect, we optimized our classifier using a wrapper-based approach for feature selection and we compared a setting where we relied on gold-standard anaphor-antecedent pairs to a setting where these had been predicted. Our results revealed a very moderate performance gain and underlined that incorporating coreference information only proves useful when integrating gold-standard coreference annotations. When coreference relations were derived automatically, this led to an overall decrease in performance because of semantic mismatches. When comparing the semantic role to the lexical-semantic features, it seemed that especially the latter features allow for a better performance. In a second setup, we investigated how to resolve implicit aspect terms. We compared a setting where gold-standard coreference resolution was used for this purpose to a setting where the implicit aspects were derived from a simple subjectivity heuristic. Our results revealed that using this heuristic results in a better coverage and performance, which means that, overall, it was difficult to find an added value in resolving coreference first. Does deep semantic information help tip the scales on performance? For Dutch readability prediction, we found that it does, when integrated in a state-of-the-art classifier. By using such information for Dutch aspect-based sentiment analysis, we found that this approach adds weight to the scales, but cannot make them tip.