UTD HLTRI at TREC 2017: Complex Answer Retrieval Track

نویسندگان

  • Ramon Maldonado
  • Stuart Taylor
  • Sanda M. Harabagiu
چکیده

This paper presents our Complex Answer PAragraph Retrieval (CAPAR) system designed for our participation in the TREC Complex Answer Retrieval (CAR) track. Because we were provided with a massive training set consisting of complex questions as well as the paragraphs that answered each aspect of the complex question, we cast the paragraph ranking as a learning to rank (L2R) problem, such that we can produce optimal results at testing time. We considered two alternative Learning to Rank (L2R) approaches for obtaining the relevance scores of each paragraph: (1) the Siamese Attention Network (SANet) for Pairwise Ranking and (2) AdaRank. The evaluation results obtained for CAPAR revealed that the Siamese Attention Network (SANet) for Pairwise Ranking outperformed AdaRank as the L2R approach for CAPAR.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

UTD HLTRI at TREC 2017: Precision Medicine Track

In this paper, we describe the system designed for the TREC 2017 Precision Medicine track by the University of Texas at Dallas (UTD) Human Language Technology Research Institute (HLTRI). Our system incorporates an aspect-based retrieval paradigm wherein each of the four structured components of the topic is cast as a separate aspect, along with two “hidden” aspects encoding the need that retrie...

متن کامل

THUIR at TREC 2004: QA

In this paper, we describe ideas and related experiments of Tsinghua University IR group in TREC 2004 QA track. In this track, our system consists three components: Question analysis, Information retrieval, and Answer extraction. Question analysis component extracts Query Term and answer type. Information retrieval component retrieves candidate documents from index set based on paragraph level ...

متن کامل

ECNU at TREC 2015: LiveQA Track

This paper reports on East Normal China University’s participation in the TREC 2015 LiveQA track. An overview is presented to introduce our community question answer system and discuss the technologies. This year, the Trec LiveQA track expands the traditional QA track, focusing on “live” question answering for the real-user questions. At this challenge, we built a real-time community question a...

متن کامل

Using Semantic Overlap Scoring in Answering TREC Relationship Questions

A first step in answering complex questions, such as those in the “Relationship” task of the Text REtrieval Conference’s Question Answering track (TREC/QA), is finding passages likely to contain pieces of the answer—passage retrieval. We introduce semantic overlap scoring, a new passage retrieval algorithm that facilitates credit assignment for inexact matches between query and candidate answer...

متن کامل

Using Profile Matching and Text Categorization for Answer Extraction in TREC Genomics

TREC’06 genomics track was focusing on text mining and passage retrieval. WIM lab participated in this year’s TREC genomics track. Our system consists of five parts: preprocessing, sentence generation, document retrieval, answer extraction and answer fusion. And we developed two different method: a automated profile matchingbased method and a text categorizationbased method to do the text minin...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2018