S $$^3$$ 3 -TM: scalable streaming short text matching

Short text matching is one of the fundamental technologies in natural language processing. In previous studies, most of the text matching networks are initially designed for English text. The common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, this method often results in word segmentation errors. Chinese short text matching faces the challenges of constructing effective features and understanding the semantic relationship between two sentences. In this work, we propose a novel lexicon-based pseudo-siamese model (CL2 N), which can fully mine the information expressed in Chinese text. Instead of utilizing a character-sequence or a single word-sequence, CL2 N augments the text representation with multi-granularity information in characters and lexicons. Additionally, it integrates sentence-level features through single-sentence features as well as interactive features. Experimental studies on two Chinese text matching datasets show that our model has better performance than the state-of-the-art short text matching models, and the proposed method can solve the error propagation problem of Chinese word segmentation. Particularly, the incorporation of single-sentence features and interactive features allows the network to capture the contextual semantics and co-attentive lexical information, which contributes to our best result.

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Long Text QA Matching Model Based on BiGRU–DAttention–DSSM

Mathematics ◽

10.3390/math9101129 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1129

Author(s):

Shihong Chen ◽

Tianjiao Xu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Language Processing ◽

Matching Task ◽

Semantic Model ◽

Text Representation ◽

Short Text ◽

Questions And Answers ◽

Text Features ◽

Text Matching

QA matching is a very important task in natural language processing, but current research on text matching focuses more on short text matching rather than long text matching. Compared with short text matching, long text matching is rich in information, but distracting information is frequent. This paper extracted question-and-answer pairs about psychological counseling to research long text QA-matching technology based on deep learning. We adjusted DSSM (Deep Structured Semantic Model) to make it suitable for the QA-matching task. Moreover, for better extraction of long text features, we also improved DSSM by enriching the text representation layer, using a bidirectional neural network and attention mechanism. The experimental results show that BiGRU–Dattention–DSSM performs better at matching questions and answers.

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Online Examination with short text matching

2014 IEEE Global Conference on Wireless Computing & Networking (GCWCN) ◽

10.1109/gcwcn.2014.7030847 ◽

2014 ◽

Cited By ~ 4

Author(s):

Pooja Kudi ◽

Amitkumar Manekar ◽

Kavita Daware ◽

Tejaswini Dhatrak

Keyword(s):

Short Text ◽

Online Examination ◽

Text Matching

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Enhanced Sentence Alignment Network for Efficient Short Text Matching

10.18653/v1/2020.wnut-1.6 ◽

2020 ◽

Author(s):

Zhe Hu ◽

Zuohui Fu ◽

Cheng Peng ◽

Weiwei Wang

Keyword(s):

Short Text ◽

Text Matching

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Neural Graph Matching Networks for Chinese Short Text Matching

10.18653/v1/2020.acl-main.547 ◽

2020 ◽

Author(s):

Lu Chen ◽

Yanbin Zhao ◽

Boer Lyu ◽

Lesheng Jin ◽

Zhi Chen ◽

...

Keyword(s):

Graph Matching ◽

Short Text ◽

Matching Networks ◽

Text Matching

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Can an Entire Introductory Research Methods Course Be Squeezed Into a Short Text?

Contemporary Psychology ◽

10.1037/002382 ◽

2001 ◽

Vol 46 (6) ◽

pp. 611-613

Author(s):

Maxwell J. Roberts

Keyword(s):

Research Methods ◽

Methods Course ◽

Short Text

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КОМУНІКАТИВНА КОМПЕТЕНТНІСТЬ У ПОЛІТИЧНОМУ ДИСКУРСІ (НА ПРИКЛАДІ ОФІЦІЙНИХ СТОРІНОК НАРОДНИХ ДЕПУТАТІВ У СОЦІАЛЬНІЙ МЕРЕЖІ «ФЕЙСБУК»)

State and Regions. Series: Social Communications ◽

10.32840/cpu2219-8741/2020.2(42).1 ◽

2020 ◽

pp. 4

Author(s):

T. Sashchuk

Keyword(s):

Social Network ◽

Subject Matter ◽

Communicative Competence ◽

Professional Activity ◽

First Person ◽

Short Text ◽

Scientific Methods ◽

Content Analysis Method ◽

General Scientific ◽

Facebook Pages

<div>The article presents the results of the study of the communicative competence of the politicians on the basis of the analysis of their messages on their official pages of the Facebook social network. The research used the following general scientific methods: descriptive and comparative, as well as analysis, synthesis and generalization. The quantitative content analysis method with qualitative elements was used to distinguish the peculiarities of information messages that provide communication of the deputies of Verkhovna Rada (Ukrainian Parliament) on their official Facebook pages. Information messages have been analyzed by the following three criteria: subject matter, structure and language.</div> For the first time the article draws a parallel between communicative competence and the ability to communicate with voters on the official pages of Facebook which is the most popular social network in Ukraine. As it is established, communicative competence in the analyzed cases is caused not by education, but by previous professional activity of a politician. The most successful and high-quality communication was from the current parliamentarian who worked as a journalist in the past. More than half of the messages that provided successful communication consisted of sufficiently structured short text and a video. The topic covers the activity of the parliamentarian in the Verkhovna Rada and in his district. More than half of the messages are spoken in the first person.The findings of the study can be used in teaching such subjects as Political PR and Electronic PR, and may be of interest to politicians and their assistants.Key words: competence and competency, communicative competence, political discourse, official page of the deputy of Verkhovna Rada of Ukraine on the Facebook social network, subject matter and structure of the information message, first-person narrative, correspondence of communication to the level of communicative competence.

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