A Linguistically Driven Framework for Query Expansion via Grammatical Constituent Highlighting and Role-Based Concept Weighting

• URL: http://arxiv.org/abs/2004.13481v1
• Date: Sat, 25 Apr 2020 01:43:00 GMT
• Title: A Linguistically Driven Framework for Query Expansion via Grammatical Constituent Highlighting and Role-Based Concept Weighting
• Authors: Bhawani Selvaretnam, Mohammed Belkhatir
• Abstract summary: Concepts-of-Interest are recognized as the core concepts that represent the gist of the search goal. The remaining query constituents which serve to specify the search goal and complete the query structure are classified as descriptive, relational or structural.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a linguistically-motivated query expansion framework that recognizes and en-codes significant query constituents that characterize query intent in order to improve retrieval performance. Concepts-of-Interest are recognized as the core concepts that represent the gist of the search goal whilst the remaining query constituents which serve to specify the search goal and complete the query structure are classified as descriptive, relational or structural. Acknowledging the need to form semantically-associated base pairs for the purpose of extracting related potential expansion concepts, an algorithm which capitalizes on syntactical dependencies to capture relationships between adjacent and non-adjacent query concepts is proposed. Lastly, a robust weighting scheme that duly emphasizes the importance of query constituents based on their linguistic role within the expanded query is presented. We demonstrate improvements in retrieval effectiveness in terms of increased mean average precision (MAP) garnered by the proposed linguistic-based query expansion framework through experimentation on the TREC ad hoc test collections.

Related papers

• Hybrid Semantic Search: Unveiling User Intent Beyond Keywords [0.0]
  This paper addresses the limitations of traditional keyword-based search in understanding user intent. It introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models.
  arXiv  Detail & Related papers  (2024-08-17T16:04:31Z)
• ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  We propose a pioneering generAtive Cross-modal rEtrieval framework (ACE) for end-to-end cross-modal retrieval. ACE achieves state-of-the-art performance in cross-modal retrieval and outperforms the strong baselines on Recall@1 by 15.27% on average.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU [0.0]
  This work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. For efficient QP, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS) The extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU)
  arXiv  Detail & Related papers  (2024-06-06T20:28:05Z)
• Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations [76.70349332096693]
  Information-seeking dialogue systems are widely used in e-commerce systems. We propose a Query-bag based Pseudo Relevance Feedback framework (QB-PRF) It constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations.
  arXiv  Detail & Related papers  (2024-03-22T08:10:32Z)
• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization [4.763356598070365]
  We propose a query-aware framework with joint modeling token and utterance based on Query-Utterance Attention. We show that the query relevance of different granularities contributes to generating a summary more related to the query.
  arXiv  Detail & Related papers  (2023-03-08T10:21:45Z)
• Query Expansion Using Contextual Clue Sampling with Language Models [69.51976926838232]
  We propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context. Our lexical matching based approach achieves a similar top-5/top-20 retrieval accuracy and higher top-100 accuracy compared with the well-established dense retrieval model DPR. For end-to-end QA, the reader model also benefits from our method and achieves the highest Exact-Match score against several competitive baselines.
  arXiv  Detail & Related papers  (2022-10-13T15:18:04Z)
• Rationale-Augmented Ensembles in Language Models [53.45015291520658]
  We reconsider rationale-augmented prompting for few-shot in-context learning. We identify rationale sampling in the output space as the key component to robustly improve performance. We demonstrate that rationale-augmented ensembles achieve more accurate and interpretable results than existing prompting approaches.
  arXiv  Detail & Related papers  (2022-07-02T06:20:57Z)
• Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval [98.62404433761432]
  The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries. We propose a Tree-augmented Cross-modal. method by jointly learning the linguistic structure of queries and the temporal representation of videos.
  arXiv  Detail & Related papers  (2020-07-06T02:50:27Z)
• Coupled intrinsic and extrinsic human language resource-based query expansion [0.0]
  We present here a query expansion framework which capitalizes on both linguistic characteristics for query constituent encoding, expansion concept extraction and concept weighting. A thorough empirical evaluation on real-world datasets validates our approach against unigram language model, relevance model and a sequential dependence based technique.
  arXiv  Detail & Related papers  (2020-04-23T11:22:38Z)
• Leveraging Cognitive Search Patterns to Enhance Automated Natural Language Retrieval Performance [0.0]
  We show that cognitive reformulation patterns that mimic user search behaviour are highlighted. We formalize the application of these patterns by considering a query conceptual representation. A genetic algorithm-based weighting process allows placing emphasis on terms according to their conceptual role-type.
  arXiv  Detail & Related papers  (2020-04-21T14:13:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.