For the TREC-2007 Genomics Track [1], we explore unsupervised techniques for extracting semantic information about biomedical concepts with a retrieval model for using these semantics in context to improve passage retrieval precision. Dependency grammar analysis is evaluated for boosting the rank of passages where complementary subject/object concept pairs can be identified between queries and ...

State-of-the-art important passage retrieval methods obtain very good results, but do not take into account privacy issues. In this paper, we present a privacy preserving method that relies on creating secure representations of documents. Our approach allows for third parties to retrieve important passages from documents without learning anything regarding their content. We use a hashing scheme...

Our Genomics experiments in this year mainly focus on improving the passage retrieval performance in the biomedical domain. We address this problem by constructing different indexes. In particular, we propose a method to build word-based index and sentence-based index for our experiments. The passage mean average precision (passage MAP) for our first run “york07ga1” using the word-based index w...

Trotman and Geva [8] suggest that XML retrieval must move from element-based to passage-based because human assessors see passages when judging relevance. Since the current XML retrieval evaluation involves returning XML elements, they suggest ways to convert passage retrieval results into XML elements. In this paper, we implemented one of their algorithms and argue that the algorithm returns a...

We propose a semantic passage segmentation method for a Question Answering (QA) system. We define a semantic passage as sentences grouped by semantic coherence, determined by the topic assigned to individual sentences. Topic assignments are done by a sentence classifier based on a statistical classification technique, Maximum Entropy (ME), combined with multiple linguistic features. We ran expe...

Dense passage retrieval aims to retrieve the relevant passages of a query from large corpus based on dense representations (i.e., vectors) and passages. Recent studies have explored improving pre-trained language models boost performance. This paper proposes CoT-MAE (ConTextual Masked Auto-Encoder), simple yet effective generative pre-training method for retrieval. employs an asymmetric encoder...

In the robust track, we mainly tested our passage-based retrieval model with different passage sizes and weighting schemes. In our approach, we used two retrieval models, namely the 2-Poisson model using BM25 term weights and the vector space model (VSM) using adaptive pivoted unique document length normalization. Also, we utilize WordNet to re-weight some PRF terms and extract some context wor...

Our group participated in the CLEF-IP 2013 Passage Retrieval starting from Claims task. We focus on formulating representative queries from various metadata that is embedded in a patent document. We then submit the queries to a state-of-the-art search engine to perform document level retrieval. For passage level retrieval, we implement a TF-IDF algorithm that calculates the sum of query keyword...

Many question-answering systems start with a passage retrieval system to facilitate the answer extraction process. The richer the set of passages, in terms of answer content, the more accurate the answer extraction. We present a simple translation model for passage retrieval at the sentence level. We demonstrate this framework on TREC data, and show that it performs better than retrieval based ...

We describe DCU’s participation in the NTCIR-12 SpokenQuery&Doc (SQD-2) task. In the context of the slide-group retrieval sub-task, we experiment with a passage retrieval method that re-scores each passage according to the relevance score of the document from which the passage is taken. This is performed by linearly interpolating their relevance scores which are calculated using the Okapi BM25 ...