Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.
Several studies have focused on using a compositional function to improve compositionality @cite_14 @cite_12 @cite_21 @cite_2 . parameterized each word as a matrix--vector combination to denote modification and representation. used a bilinear function enacted by tensor slicing for composition in place of a large matrix--vector parameterization. incorporated a constituent label of each phrase as feature embedding to take account of the different compositionality based on parent or children label combinations. proposed a more robust model than those discussed above in which long short-term memory (LSTM) units were applied to a RvNN, as mentioned in Section . Thanks to the application of LSTM, this model can learn increased numbers of parameters appropriately, unlike the other models.
@cite_14 @cite_21 @cite_12 @cite_2
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The field of distributional-compositional semantics has yielded a range of computational models for composing the vector of a phrase from those of constituent word vectors. Existing models have various ranges of their expressiveness, recursivity, and trainability. However, these models have not been examined closely for their compositionality. We implement and compare these models under the same conditions. The experimentally obtained results demonstrate that the model using different composition matrices for different dependency relations achieved state-ofthe-art performance on a dataset for two-word compositions (Mitchell and Lapata, 2010). Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them. In this paper we explore the computational modelling of compositionality in distributional models of semantics. In particular, we model the semantic composition of pairs of adjacent English Adjectives and Nouns from the British National Corpus. We build a vector-based semantic space from a lemmatised version of the BNC, where the most frequent A-N lemma pairs are treated as single tokens. We then extrapolate three different models of compositionality: a simple additive model, a pointwise-multiplicative model and a Partial Least Squares Regression (PLSR) model. We propose two evaluation methods for the implemented models. Our study leads to the conclusion that regression-based models of compositionality generally out-perform additive and multiplicative approaches, and also show a number of advantages that make them very promising for future research. Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characterized by a memory state that can be written to and read from by applying gated composition operations to the current input and the previous state. However, they only cover a small subset of potentially useful compositions. We propose Multi-Function Recurrent Units (MuFuRUs) that allow for arbitrary differentiable functions as composition operations. Furthermore, MuFuRUs allow for an input- and state-dependent choice of these composition operations that is learned. Our experiments demonstrate that the additional functionality helps in different sequence modeling tasks, including the evaluation of propositional logic formulae, language modeling and sentiment analysis.