sentence similarity

Structured Optimal Transport Machine Learning

Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capture frequently occurring structure beyond the "ground metric" is limited. In this work, we develop a nonlinear generalization of (discrete) optimal transport that is able to reflect much additional structure. We demonstrate how to leverage the geometry of this new model for fast algorithms, and explore connections and properties. Illustrative experiments highlight the benefit of the induced structured couplings for tasks in domain adaptation and natural language processing.

Structural Sentence Similarity Estimation for Short Texts

AAAI Conferences

Sentence similarity is the basis of most text-related tasks. In this paper, we define a new task of sentence similarity estimation specifically for short while informal, social-network styled sentences. The new type of sentence similarity, which we call Structural Similarity, eliminates syntactic or grammatical features such as dependency paths and Part-of-Speech (POS) tagging which do not have enough representativeness on short sentences. Structural Similarity does not consider actual meanings of the sentences either but puts more emphasis on the similarities of sentence structures, so as to discover purpose- or emotion-level similarities. The idea is based on the observation that people tend to use sentences with similar structures to express similar feelings. Besides the definition, we present a new feature set and a mechanism to calculate the scores, and, for the needs of disambiguating word senses we propose a variant of the Word2Vec model to represent words. We prove the correctness and advancement of our sentence similarity measurement by experiments.

Optimizing Sentence Modeling and Selection for Document Summarization

AAAI Conferences

Extractive document summarization aims to conclude given documents by extracting some salient sentences. Often, it faces two challenges: 1) how to model the information redundancy among candidate sentences; 2) how to select the most appropriate sentences. This paper attempts to build a strong summarizer DivSelect+CNNLM by presenting new algorithms to optimize each of them. Concretely, it proposes CNNLM, a novel neural network language model (NNLM) based on convolutional neural network (CNN), to project sentences into dense distributed representations, then models sentence redundancy by cosine similarity. Afterwards, it formulates the selection process as an optimization problem, constructing a diversified selection process (DivSelect) with the aim of selecting some sentences which have high prestige, meantime, are dis-similar with each other. Experimental results on DUC2002 and DUC2004 benchmark data sets demonstrate the effectiveness of our approach.