Igor Malioutov


2020

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NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg
Joshua Bambrick | Minjie Xu | Andy Almonte | Igor Malioutov | Guim Perarnau | Vittorio Selo | Iat Chong Chan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day. Consuming such a volume of news presents an almost insurmountable challenge. For example, a reader searching on Bloomberg’s system for news about the U.K. would find 10,000 articles on a typical day. Apple Inc., the world’s most journalistically covered company, garners around 1,800 news articles a day. We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points. The system would filter out noise and duplicates to identify and summarize key news about companies, countries or markets. When given a user query, Bloomberg’s solution, Key News Themes (or NSTM), leverages state-of-the-art semantic clustering techniques and novel summarization methods to produce comprehensive, yet concise, digests to dramatically simplify the news consumption process. NSTM is available to hundreds of thousands of readers around the world and serves thousands of requests daily with sub-second latency. At ACL 2020, we will present a demo of NSTM.

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Semantic Role Labeling as Syntactic Dependency Parsing
Tianze Shi | Igor Malioutov | Ozan Irsoy
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.

2013

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Language Acquisition and Probabilistic Models: keeping it simple
Aline Villavicencio | Marco Idiart | Robert Berwick | Igor Malioutov
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2007

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Making Sense of Sound: Unsupervised Topic Segmentation over Acoustic Input
Igor Malioutov | Alex Park | Regina Barzilay | James Glass
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Minimum Cut Model for Spoken Lecture Segmentation
Igor Malioutov | Regina Barzilay
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics