Mehdi Manshadi


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A Notion of Semantic Coherence for Underspecified Semantic Representation
Mehdi Manshadi | Daniel Gildea | James F. Allen
Computational Linguistics, Volume 44, Issue 1 - April 2018

The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.


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The CMU METAL Farsi NLP Approach
Weston Feely | Mehdi Manshadi | Robert Frederking | Lori Levin
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

While many high-quality tools are available for analyzing major languages such as English, equivalent freely-available tools for important but lower-resourced languages such as Farsi are more difficult to acquire and integrate into a useful NLP front end. We report here on an accurate and efficient Farsi analysis front end that we have assembled, which may be useful to others who wish to work with written Farsi. The pre-existing components and resources that we incorporated include the Carnegie Mellon TurboParser and TurboTagger (Martins et al., 2010) trained on the Dadegan Treebank (Rasooli et al., 2013), the Uppsala Farsi text normalizer PrePer (Seraji, 2013), the Uppsala Farsi tokenizer (Seraji et al., 2012a), and Jon Dehdari’s PerStem (Jadidinejad et al., 2010). This set of tools (combined with additional normalization and tokenization modules that we have developed and made available) achieves a dependency parsing labeled attachment score of 89.49%, unlabeled attachment score of 92.19%, and label accuracy score of 91.38% on a held-out parsing test data set. All of the components and resources used are freely available. In addition to describing the components and resources, we also explain the rationale for our choices.


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Plurality, Negation, and Quantification:Towards Comprehensive Quantifier Scope Disambiguation
Mehdi Manshadi | Daniel Gildea | James Allen
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Expanding the Range of Tractable Scope-Underspecified Semantic Representations
Mehdi Manshadi | James Allen
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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An Annotation Scheme for Quantifier Scope Disambiguation
Mehdi Manshadi | James Allen | Mary Swift
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Annotating natural language sentences with quantifier scoping has proved to be very hard. In order to overcome the challenge, previous work on building scope-annotated corpora has focused on sentences with two explicitly quantified noun phrases (NPs). Furthermore, it does not address the annotation of scopal operators or complex NPs such as plurals and definites. We present the first annotation scheme for quantifier scope disambiguation where there is no restriction on the type or the number of scope-bearing elements in the sentence. We discuss some of the most prominent complex scope phenomena encountered in annotating the corpus, such as plurality and type-token distinction, and present mechanisms to handle those phenomena.


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Unrestricted Quantifier Scope Disambiguation
Mehdi Manshadi | James Allen
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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A Corpus of Scope-disambiguated English Text
Mehdi Manshadi | James Allen | Mary Swift
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Semantic Tagging of Web Search Queries
Mehdi Manshadi | Xiao Li
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP


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Deep Linguistic Processing for Spoken Dialogue Systems
James Allen | Myroslava Dzikovska | Mehdi Manshadi | Mary Swift
ACL 2007 Workshop on Deep Linguistic Processing