Overview of the TREC 2001 Question Answering Track

By Ellen M. Voorhees. 2001

248 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning (2017, methodology): “We evaluate the quality of the encoder learned by our system, which we call DiscSent, by using the sentence representations it produces in a variety of sentence classiﬁ-Model et al., 2004), subjectivity evaluation (SUBJ; Pang and Lee, 2004) and question classiﬁcation (TREC; Voorhees, 2001).”
ERNIE: Enhanced Representation through Knowledge Integration (2019, methodology): “The evaluation methods on this dataset include MRR (Voorhees, 2001) and F1 score.”
Latent Retrieval for Weakly Supervised Open Domain Question Answering (2019, background): “The questions come from various sources of real queries, such as MSNSearch or AskJeeves logs, where the question askers do not observe any evidence documents (Voorhees, 2001).”
Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “st then manually peruse. Although the history of question answering systems dates back to at least the 1960s [Simmons, 1965], modern approaches trace their roots to work that began in the late 1990s [Voorhees, 2001]. In the web context, question answering capabilities are often exposed in search eng”
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (2017, methodology): “We will make our code and pre-trained models available open-source.”
Open-domain textual question answering techniques (2003, result): “As reported in Voorhees (2001), the results of this task were unexpected.”
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking (2007, background): “This argument lead to a ranking measure called Normalized Discounted Cumulative Gain[16]:”
Overview of the TREC 2003 Question Answering Track (2004, background): “Definition questions occur relatively frequently in logs of web search engines [4] suggest ing they are an important type of question.”
What Makes a Good Answer? The Role of Context in Question Answering (2003, background): “Developments in question answering have focused on improving system performance against a standard set of questions; the QA track at the TREC conferences (Voorhees, 2001; Voorhees, 2002) is a notable example.”
Modelling input texts: from Tree Kernels to Deep Learning (2015, background): “, carried out in TREC [169–171], has shown that such an approach can lead to the design of accurate systems.”
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List (2009, background): “Some of these objectives are similar to current popular quality measurements from information retrieval (IR), such as the “mean reciprocal rank” (MRR) (Voorhees, 2001) and “discounted cumulative gain” (DCG) (Järvelin and Kekäläinen, 2000).”
Quantitative evaluation of passage retrieval algorithms for question answering (2003, methodology): “[24] E. M. Voorhees.”
Syntactic and Semantic Kernels for Short Text Pair Categorization (2009, unknown): “In contrast, work in question answering suggests that syntactic and semantic structures help in solving TC (Voorhees, 2004; Hickl et al., 2006).”