Eugene Agichtein


2022

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Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)
Shervin Malmasi | Oleg Rokhlenko | Nicola Ueffing | Ido Guy | Eugene Agichtein | Surya Kallumadi
Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)

2021

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Proceedings of The 4th Workshop on e-Commerce and NLP
Shervin Malmasi | Surya Kallumadi | Nicola Ueffing | Oleg Rokhlenko | Eugene Agichtein | Ido Guy
Proceedings of The 4th Workshop on e-Commerce and NLP

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Identifying Helpful Sentences in Product Reviews
Iftah Gamzu | Hila Gonen | Gilad Kutiel | Ran Levy | Eugene Agichtein
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.

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You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions
Sergey Volokhin | Joyce Ho | Oleg Rokhlenko | Eugene Agichtein
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user’s interest vector, and adapting collaborative filtering techniques to estimate the current user’s preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user’s sentiment towards an entity from the conversation context, and 2) transforms the ratings of “similar” external reviewers to predict the current user’s preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent. Our results demonstrate that ConvExtr can improve the accuracy of predicting users’ ratings for new movies by exploiting conversation content and external data.

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VoiSeR: A New Benchmark for Voice-Based Search Refinement
Simone Filice | Giuseppe Castellucci | Marcus Collins | Eugene Agichtein | Oleg Rokhlenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Voice assistants, e.g., Alexa or Google Assistant, have dramatically improved in recent years. Supporting voice-based search, exploration, and refinement are fundamental tasks for voice assistants, and remain an open challenge. For example, when using voice to search an online shopping site, a user often needs to refine their search by some aspect or facet. This common user intent is usually available through a “filter-by” interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets. To our knowledge, no benchmark dataset exists for training or validating such contextual search understanding models. To bridge this gap, we introduce the first large-scale dataset of voice-based search refinements, VoiSeR, consisting of about 10,000 search refinement utterances, collected using a novel crowdsourcing task. These utterances are intended to refine a previous search, with respect to a search facet or attribute (e.g., brand, color, review rating, etc.), and are manually annotated with the specific intent. This paper reports qualitative and empirical insights into the most common and challenging types of refinements that a voice-based conversational search system must support. As we show, VoiSeR can support research in conversational query understanding, contextual user intent prediction, and other conversational search topics to facilitate the development of conversational search systems.

2020

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Proceedings of The 3rd Workshop on e-Commerce and NLP
Shervin Malmasi | Surya Kallumadi | Nicola Ueffing | Oleg Rokhlenko | Eugene Agichtein | Ido Guy
Proceedings of The 3rd Workshop on e-Commerce and NLP

2017

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EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering
Denis Savenkov | Eugene Agichtein
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering.

2016

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Crowdsourcing for (almost) Real-time Question Answering
Denis Savenkov | Scott Weitzner | Eugene Agichtein
Proceedings of the Workshop on Human-Computer Question Answering

2015

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Relation Extraction from Community Generated Question-Answer Pairs
Denis Savenkov | Wei-Lwun Lu | Jeff Dalton | Eugene Agichtein
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

2014

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Towards Tracking Political Sentiment through Microblog Data
Yu Wang | Tom Clark | Jeffrey Staton | Eugene Agichtein
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

2013

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The Answer is at your Fingertips: Improving Passage Retrieval for Web Question Answering with Search Behavior Data
Mikhail Ageev | Dmitry Lagun | Eugene Agichtein
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2010

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Query Ambiguity Revisited: Clickthrough Measures for Distinguishing Informational and Ambiguous Queries
Yu Wang | Eugene Agichtein
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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The “Nays” Have It: Exploring Effects of Sentiment in Collaborative Knowledge Sharing
Ablimit Aji | Eugene Agichtein
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Towards Automatic Question Answering over Social Media by Learning Question Equivalence Patterns
Tianyong Hao | Wenyin Liu | Eugene Agichtein
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

2008

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You’ve Got Answers: Towards Personalized Models for Predicting Success in Community Question Answering
Yandong Liu | Eugene Agichtein
Proceedings of ACL-08: HLT, Short Papers

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Tutorial Abstracts of ACL-08: HLT
Ani Nenkova | Marilyn Walker | Eugene Agichtein
Tutorial Abstracts of ACL-08: HLT

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CoCQA: Co-Training over Questions and Answers with an Application to Predicting Question Subjectivity Orientation
Baoli Li | Yandong Liu | Eugene Agichtein
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

1998

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NYU: Description of the MENE Named Entity System as Used in MUC-7
Andrew Borthwick | John Sterling | Eugene Agichtein | Ralph Grishman
Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998

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Exploiting Diverse Knowledge Sources via Maximum Entropy in Named Entity Recognition
Andrew Borthwick | John Sterling | Eugene Agichtein | Ralph Grishman
Sixth Workshop on Very Large Corpora