Compositional Distributional Semantics with Long Short Term Memory

We are proposing an extension of the recursive neural network that makes use of a variant of the long short-term memory architecture. The extension allows information low in parse trees to be stored in a memory register (the `memory cell') and used much later higher up in the parse tree. This provides a solution to the vanishing gradient problem and allows the network to capture long range dependencies. Experimental results show that our composition outperformed the traditional neural-network composition on the Stanford Sentiment Treebank.Comment: 10 pages, 7 figure

Dynamic Compositional Neural Networks over Tree Structure

Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information. In spite of their success, most existing models suffer from the underfitting problem: they recursively use the same shared compositional function throughout the whole compositional process and lack expressive power due to inability to capture the richness of compositionality. In this paper, we address this issue by introducing the dynamic compositional neural networks over tree structure (DC-TreeNN), in which the compositional function is dynamically generated by a meta network. The role of meta-network is to capture the metaknowledge across the different compositional rules and formulate them. Experimental results on two typical tasks show the effectiveness of the proposed models.Comment: Accepted by IJCAI 201

MS-TR: A Morphologically Enriched Sentiment Treebank and Recursive Deep Models for Compositional Semantics in Turkish

Recursive Deep Models have been used as powerful models to learn compositional representations of text for many natural language processing tasks. However, they require structured input (i.e. sentiment treebank) to encode sentences based on their tree-based structure to enable them to learn latent semantics of words using recursive composition functions. In this paper, we present our contributions and efforts for the Turkish Sentiment Treebank construction. We introduce MS-TR, a Morphologically Enriched Sentiment Treebank, which was implemented for training Recursive Deep Models to address compositional sentiment analysis for Turkish, which is one of the well-known Morphologically Rich Language (MRL). We propose a semi-supervised automatic annotation, as a distantsupervision approach, using morphological features of words to infer the polarity of the inner nodes of MS-TR as positive and negative. The proposed annotation model has four different annotation levels: morph-level, stem-level, token-level, and review-level. Each annotation level’s contribution was tested using three different domain datasets, including product reviews, movie reviews, and the Turkish Natural Corpus essays. Comparative results were obtained with the Recursive Neural Tensor Networks (RNTN) model which is operated over MS-TR, and conventional machine learning methods. Experiments proved that RNTN outperformed the baseline methods and achieved much better accuracy results compared to the baseline methods, which cannot accurately capture the aggregated sentiment information