DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY

نویسندگان

چکیده

Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, linking, coreference resolution, schema/ontology text similarity, textual entailment, question answering, tagging, etc. has received much attention in the database, AI, KDD, Web, and Web communities. Recently, many works have also applied deep learning (DL) to matching. In this paper we survey fast growing topic. We define problem, categorize its variations into a taxonomy, describe important applications. DL solutions for Finally, discuss future R\&D directions.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Deep Semantic Matching for Optical Flow

We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participan...

متن کامل

Deep Fusion LSTMs for Text Semantic Matching

Recently, there is rising interest in modelling the interactions of text pair with deep neural networks. In this paper, we propose a model of deep fusion LSTMs (DF-LSTMs) to model the strong interaction of text pair in a recursive matching way. Specifically, DF-LSTMs consist of two interdependent LSTMs, each of which models a sequence under the influence of another. We also use external memory ...

متن کامل

Deep Learning for Semantic Parsing

Recently, we developed USP, the first approach for unsupervised semantic parsing [11]. We applied it to extracting a knowledge base from biomedical abstracts for question answering and found that it substantially outperforms state-of-the-art systems such as TextRunner and DIRT. In this paper, we show that USP can be viewed as learning a deep network for semantic parsing. The hidden units in the...

متن کامل

Deep Learning for Semantic Composition

Learning representations to model the meaning of text has been a core problem in natural language understanding (NLP). The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as continuous vectors. If properly learned, such representations have been shown to help achieve the state-of-the-art performances on a ...

متن کامل

Deep Learning for Semantic Similarity

Evaluating the semantic similarity of two sentences is a task central to automated understanding of natural languages. We discuss the problem of semantic similarity and show that the use of recurrent and recursive neural networks can provide a 16% to 70% improvement over baseline models.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Computer Science and Cybernetics

سال: 2021

ISSN: ['1813-9663']

DOI: https://doi.org/10.15625/1813-9663/37/4/16151