Finding Optimal 1-Endpoint-Crossing Trees

Pitler, Emily; Kannan, Sampath; Marcus, Mitchell P.

doi:10.1162/tacl_a_00206

Cited by 39 publications

(68 citation statements)

References 13 publications

(17 reference statements)

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“…Such dependencies are interesting because while they exist across languages, they are nevertheless rare (Ferrer‐i Cancho, 2006; Straka, Hajic, Straková, & Hajic, 2015), and they help in understanding formal properties of natural languages (Joshi, 1985; Kuhlmann & Möhl, 2007; Shieber, 1985). More importantly, they are difficult for computational parsers (Gómez‐Rodríguez, Weir, & Carroll, 2009; Kübler et al, 2009; Kuhlmann & Satta, 2009; McDonald & Satta, 2007; Nederhof, 1999; Neuhaus & Bröker, 1997; Nivre, 2009; Pitler, Kannan, & Marcus, 2013; Vijay‐Shankar & Joshi, 1985), and they are known to pose difficulty for humans (Boland, Tanenhaus, Garnsey, & Carlson, 1995; Husain & Vasishth, 2015; Levy et al, 2012; Traxler & Pickering, 1996; Yadav, Vaidya, & Husain, 2017).…”

Section: Methodsmentioning

confidence: 99%

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

et al. 2020

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Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results Correspondence should be sent to Himanshu Yadav is now affiliated with University of Potsdam, Germany. Vishakha Shukla is now affiliated with Shroff Charity Eye Hospital, Delhi, India. show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

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Section: Methodsmentioning

confidence: 99%

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

et al. 2020

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“…Unfortunately, exact inference has been shown to be intractable for models that support arbitrary non-projectivity, except under strong independence assumptions (McDonald and Satta 2007) which enable parsing in quadratic time with maximum spanning tree algorithms (McDonald et al 2005), but severely limit the expressivity of the feature models that can be used. This restriction can be avoided by using so-called mildly non-projective parsing algorithms, which support the overwhelming majority of non-projective analyses that can be found in real linguistic structures (Gómez-Rodríguez et al 2011; Cohen et al 2011;Pitler et al 2013); but they have supercubic complexities that make them too slow for practical use. Another option is to forgo exact inference, using approximate inference algorithms with rich feature models instead.…”

Section: Parsingmentioning

confidence: 99%

How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis

Gómez-Rodríguez¹,

Alonso-Alonso²,

Vilares³

2017

Artif Intell Rev

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not reflect subsequent changes resulting from the publishing process such as editing, formatting, pagination, and other quality control mechanisms. The final publication in Artificial Intelligence Review is available at link.springer.com via http://dx.doi.org/10.1007/s10462-017-9584-0.Abstract Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful.In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources.The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.

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“…Our proof is very similar in style and structure to Pitler et al (2013). The general approach is to consider the set of structures an item could represent, and divide them into cases based on properties of the internal structure.…”

Section: Propertiesmentioning

confidence: 99%

“…Note that by using subsets of our rules, we can restrict the space of structures we generate, giving parsing algorithms for projective DAGs, projective trees (Eisner 1996), or 1-EC trees (Pitler et al 2013). Also, versions of these spaces with undirected edges could be easily handled with the same approach.…”

Section: Propertiesmentioning

confidence: 99%

“…For constituency parsing, a range of formalisms have been developed that are mildlycontext sensitive, such as CCG (Steedman 2000), LFG (Kaplan and Bresnan 1982), and LTAG (Joshi and Schabes 1997). Concurrently with this work, Cao et al (2017) also proposed a graph version of Pitler et al (2013)'s One-Endpoint Crossing (1-EC) algorithm. However, Cao's algorithm does not consider the direction of edges 1 and so it could produce cycles, or graphs with multiple root nodes.…”

Section: Introductionmentioning

confidence: 97%

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Parsing with Traces: An O(n⁴) Algorithm and a Structural Representation

Kummerfeld

Klein

2017

TACL

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General treebank analyses are graph structured, but parsers are typically restricted to tree structures for efficiency and modeling reasons. We propose a new representation and algorithm for a class of graph structures that is flexible enough to cover almost all treebank structures, while still admitting efficient learning and inference. In particular, we consider directed, acyclic, one-endpoint-crossing graph structures, which cover most long-distance dislocation, shared argumentation, and similar tree-violating linguistic phenomena. We describe how to convert phrase structure parses, including traces, to our new representation in a reversible manner. Our dynamic program uniquely decomposes structures, is sound and complete, and covers 97.3% of the Penn English Treebank. We also implement a proofof-concept parser that recovers a range of null elements and trace types.

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Finding Optimal 1-Endpoint-Crossing Trees

Cited by 39 publications

References 13 publications

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis

Parsing with Traces: An O(n⁴) Algorithm and a Structural Representation

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Finding Optimal 1-Endpoint-Crossing Trees

Cited by 39 publications

References 13 publications

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis

Parsing with Traces: An O(n4) Algorithm and a Structural Representation

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Parsing with Traces: An O(n⁴) Algorithm and a Structural Representation