Matching Natural Language Sentences with Hierarchical Sentence Factorization

Liu, Bang; Zhang, Ting; Han, Fred X.; Niu, Di; Lai, K. Robert; Xu, Yu

doi:10.1145/3178876.3186022

Cited by 22 publications

(10 citation statements)

References 34 publications

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“…Some CNN-based models use multiple types of pooling [4][5] or long-range semantic dependencies between words [6] to improve their performance. RNNs can model the sequential information in text and obtain global semantic information [7][8] [9]. There are also several hybrid models based on CNNs and RNNs [10][11].…”

Section: Related Workmentioning

confidence: 99%

An Approach for Buyer Name Normalization in Pharmacy Sales Data

Wang

Nie

et al. 2021

IEEE Access

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It is fundamental for pharmaceutical enterprises to make statistics and analyses on the massive sales data that are submitted by distributors at different levels when making market plans and other decisions. However, buyer names in sales data may have various forms due to aliases, abbreviations, typos, and other reasons, which severely affect the subsequent mining performance. To tackle this problem, in this paper, we propose a novel approach called BuyerNorm which can identify different expression forms for a given buyer name. The proposed model takes pairs of variable length buyer names as input and combines Bidirectional Encoder Representations from Transformers (BERT) and Bi-directional Long Short-Term Memory (BiLSTM) to obtain string representations and calculate the similarity between the two names, which indicates whether they represent the same buyer. For this task, we first build a data set including more than 80,000 pairs of buyer names. Then, extensive experiments on the data set show that BuyerNorm performs better than the state-of-the-art baseline and can obtain an average AUC of 99.84%.

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Section: Related Workmentioning

confidence: 99%

An Approach for Buyer Name Normalization in Pharmacy Sales Data

Wang

Nie

et al. 2021

IEEE Access

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“…Kusner et al proposed the Word Mover's Distance (WMD) [33] to measure the distance between text documents. Liu et al proposed a hierarchical model for sentences matching and extended WMD to the Ordered WMD [34] that considers the positions of words in a sentence. Song et al also proposed a positional convolution neural model (P-CNN) [35] that takes the sequential structures of sentences into account.…”

Section: Related Work a Text Matchingmentioning

confidence: 99%

Enhanced Text Matching Based on Semantic Transformation

et al. 2020

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Text matching is the core of natural language processing (NLP) system. It's considered as a touchstone of the NLP, and it aims to find whether text pairs are equal in semantics. However, the semantic gap in text matching is still an open problem to solve. Inspired by successes of cycle-consistent adversarial network (CycleGAN) in image domain transformation, we propose an enhanced text matching method based on the CycleGAN combined with the Transformer network. Based on the proposed method, the text semantics in a source domain is transferred to a similar or different target domain, and the semantic distance between text pairs is decreased. Meanwhile, we demonstrate our method in paraphrase identification and question answer matching. The matching degree is computed by a standard text matching model to evaluate the transforming influence on narrowing the text semantic gap. The experiments show that our method achieves text domain adaptation, and the effects on different matching models are remarkable. INDEX TERMS Cycle-consistent adversarial network, domain adaptation, semantic transformation, text matching, transformer network.

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“…Multi-semantic methods and direct modeling methods have proven to be more effective than single-text based methods in many studies [17], and both have the advantages over different matching tasks. This paper proposes a multi-perspective semantic crossover model-MPSC to model two texts.…”

Section: Related Workmentioning

confidence: 99%

MPSC: A Multiple-Perspective Semantics-Crossover Model for Matching Sentences

Peng

Liu

2019

IEEE Access

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Sentence matching is crucial to many natural language processing (NLP) tasks. Generally, the degree of matching is measured from either of the two perspectives: topic-based match or semantic-based match. The former is to investigate if two sentences discuss the same topic, and the latter performs a deep level semantic matching of texts, which is currently the highlight in research. Deep semantic matching requires adequate modeling from the internal structure of the language objects as well as their interactions. To achieve this goal, this paper proposes a multiple-perspective semantics-crossover (MPSC) model for modeling the semantic-based match of two sentences. The model extracts the matching information of two sentences from the semantic interaction information generated from different angles, so as to calculate the matching degree of the two sentences. The MPSC model not only captures rich matching patterns at different levels but also acquires interactive features from different semantic angles. It can be used to address some important issues in NLP fields, such as information matching in text retrieval, question-answer matching in the Q&A system, and so on. The experimental results show that our proposed model of MPSC has better effectiveness than some popular semantic matching approaches.INDEX TERMS Natural language processing, neural networks, semantics matching, text analysis.

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Matching Natural Language Sentences with Hierarchical Sentence Factorization

Cited by 22 publications

References 34 publications

An Approach for Buyer Name Normalization in Pharmacy Sales Data

An Approach for Buyer Name Normalization in Pharmacy Sales Data

Enhanced Text Matching Based on Semantic Transformation

MPSC: A Multiple-Perspective Semantics-Crossover Model for Matching Sentences

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