Search Results for author: Khyathi u

Found 8 papers, 0 papers with code

Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions

no code implementations WS 2018 Yutong Li, Nicholas Gekakis, Qiuze Wu, Boyue Li, Ch, Khyathi u, Eric Nyberg

The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers.

Abstractive Text Summarization Answer Generation +4

Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation

no code implementations WS 2018 Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Ch, Khyathi u, Teruko Mitamura, Eric Nyberg

The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine.

Abstractive Text Summarization Answer Generation +3

Tackling Code-Switched NER: Participation of CMU

no code implementations WS 2018 Parvathy Geetha, Ch, Khyathi u, Alan W. black

In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data.

named-entity-recognition NER +1

Language Informed Modeling of Code-Switched Text

no code implementations WS 2018 Ch, Khyathi u, Thomas Manzini, Sumeet Singh, Alan W. black

Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.

Machine Translation Speech Recognition

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations WS 2018 Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).

Question Answering

Tackling Biomedical Text Summarization: OAQA at BioASQ 5B

no code implementations WS 2017 Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg

In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data.

Answer Generation Extractive Summarization +2

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