https://github.com/ielab/tilde
공식 코드Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Information retrieval is the task of ranking a list of documents or search results in response to a query
<span style="color:grey; opacity: 0.6">( Image credit: sudhanshumittal )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval
CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example
Learning music audio representations via weak language supervision
Conversational Entity Linking: Problem Definition and Datasets
CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion
TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents
REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources
repro_eval: A Python Interface to Reproducibility Measures of System-oriented IR Experiments
Evaluating Contrastive Feedback for Effective User Simulations
Context-Driven Interactive Query Simulations Based on Generative Large Language Models
ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
Quantum-inspired Embeddings Projection and Similarity Metrics for Representation Learning
VerbCL: A Dataset of Verbatim Quotes for Highlight Extraction in Case Law
BULNER: BUg Localization with word embeddings and NEtwork Regularization
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Quantifying Musical Style: Ranking Symbolic Music based on Similarity to a Style
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Asynchronous Training of Word Embeddings for Large Text Corpora
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.