Mohammad Javad Hosseini


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Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou | Sander Bijl de Vroe | Mohammad Javad Hosseini | Mark Johnson | Mark Steedman
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.


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Duality of Link Prediction and Entailment Graph Induction
Mohammad Javad Hosseini | Shay B. Cohen | Mark Johnson | Mark Steedman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.


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Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini | Nathanael Chambers | Siva Reddy | Xavier R. Holt | Shay B. Cohen | Mark Johnson | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 6

This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.


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UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields
Mohammad Javad Hosseini | Noah A. Smith | Su-In Lee
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Learning to Solve Arithmetic Word Problems with Verb Categorization
Mohammad Javad Hosseini | Hannaneh Hajishirzi | Oren Etzioni | Nate Kushman
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)