The optimal placement of the head in the noun phrase. The case of demonstrative, numeral, adjective and noun

• URL: http://arxiv.org/abs/2402.10311v5
• Date: Fri, 22 Mar 2024 16:10:14 GMT
• Title: The optimal placement of the head in the noun phrase. The case of demonstrative, numeral, adjective and noun
• Authors: Ramon Ferrer-i-Cancho,
• Abstract summary: We show that, across preferred orders in languages, the noun tends to be placed at one of the ends. We also show evidence of anti locality effects: syntactic dependency in preferred orders are longer than expected by chance.
• Score: 0.16317061277456998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The word order of a sentence is shaped by multiple principles. The principle of syntactic dependency distance minimization is in conflict with the principle of surprisal minimization (or predictability maximization) in single head syntactic dependency structures: while the former predicts that the head should be placed at the center of the linear arrangement, the latter predicts that the head should be placed at one of the ends (either first or last). A critical question is when surprisal minimization (or predictability maximization) should surpass syntactic dependency distance minimization. In the context of single head structures, it has been predicted that this is more likely to happen when two conditions are met, i.e. (a) fewer words are involved and (b) words are shorter. Here we test the prediction on the noun phrase when it is composed of a demonstrative, a numeral, an adjective and a noun. We find that, across preferred orders in languages, the noun tends to be placed at one of the ends, confirming the theoretical prediction. We also show evidence of anti locality effects: syntactic dependency distances in preferred orders are longer than expected by chance.

Related papers

• Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS [68.34155010428941]
  It is unclear what kind of sentence pairs a sentence encoder (SE) would consider similar. HEROS is constructed by transforming an original sentence into a new sentence based on certain rules to form a textitminimal pair By systematically comparing the performance of over 60 supervised and unsupervised SEs on HEROS, we reveal that most unsupervised sentence encoders are insensitive to negation.
  arXiv  Detail & Related papers  (2023-06-08T10:24:02Z)
• A bounded rationality account of dependency length minimization in Hindi [0.0]
  The principle of DEPENDENCY LENGTH MINIMIZATION is thought to shape the structure of human languages for effective communication. Preverbally, the placement of long-before-short constituents and postverbally, short-before-long constituents are known to minimize overall dependency length of a sentence. In this study, we test the hypothesis that placing only the shortest preverbal constituent next to the main-verb explains word order preferences in Hindi.
  arXiv  Detail & Related papers  (2023-04-22T13:53:50Z)
• The distribution of syntactic dependency distances [0.7614628596146599]
  We contribute to the characterization of the actual distribution of syntactic dependency distances. We propose a new double-exponential model in which decay in probability is allowed to change after a break-point. We find that a two-regime model is the most likely one in all 20 languages we considered.
  arXiv  Detail & Related papers  (2022-11-26T17:31:25Z)
• Neuro-Symbolic Entropy Regularization [78.16196949641079]
  In structured prediction, the goal is to jointly predict many output variables that together encode a structured object. One approach -- entropy regularization -- posits that decision boundaries should lie in low-probability regions. We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object.
  arXiv  Detail & Related papers  (2022-01-25T06:23:10Z)
• Dependency distance minimization predicts compression [1.2944868613449219]
  Dependency distance minimization (DDm) is a well-established principle of word order. This is a second order prediction because it links a principle with another principle, rather than a principle and a manifestation as in a first order prediction. We use a recently introduced score that has many mathematical and statistical advantages with respect to the widely used sum of dependency distances.
  arXiv  Detail & Related papers  (2021-09-18T10:53:39Z)
• Rationales for Sequential Predictions [117.93025782838123]
  Sequence models are a critical component of modern NLP systems, but their predictions are difficult to explain. We consider model explanations though rationales, subsets of context that can explain individual model predictions. We propose an efficient greedy algorithm to approximate this objective.
  arXiv  Detail & Related papers  (2021-09-14T01:25:15Z)
• Semantic-Preserving Adversarial Text Attacks [85.32186121859321]
  We propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models. Our method achieves the highest attack success rates and semantics rates by changing the smallest number of words compared with existing methods.
  arXiv  Detail & Related papers  (2021-08-23T09:05:18Z)
• Linear-time calculation of the expected sum of edge lengths in random projective linearizations of trees [1.2944868613449219]
  The sum of distances between syntactically related words has been in the limelight for the past decades. Various random baselines have been defined to carry out related quantitative studies on languages. Here we focus on a popular baseline: random projective permutations of the words of the sentence.
  arXiv  Detail & Related papers  (2021-07-07T15:11:53Z)
• Topology of Word Embeddings: Singularities Reflect Polysemy [68.8204255655161]
  We introduce a topological measure of polysemy based on persistent homology that correlates well with the actual number of meanings of a word. We propose a simple, topologically motivated solution to the SemEval-2010 task on Word Sense Induction & Disambiguation.
  arXiv  Detail & Related papers  (2020-11-18T17:21:51Z)
• The optimality of syntactic dependency distances [0.802904964931021]
  We recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network. We introduce a new score to quantify the cognitive pressure to reduce the distance between linked words in a sentence. The analysis of sentences from 93 languages reveals that half of languages are optimized to a 70% or more.
  arXiv  Detail & Related papers  (2020-07-30T09:40:41Z)
• Invariant Rationalization [84.1861516092232]
  A typical rationalization criterion, i.e. maximum mutual information (MMI), finds the rationale that maximizes the prediction performance based only on the rationale. We introduce a game-theoretic invariant rationalization criterion where the rationales are constrained to enable the same predictor to be optimal across different environments. We show both theoretically and empirically that the proposed rationales can rule out spurious correlations, generalize better to different test scenarios, and align better with human judgments.
  arXiv  Detail & Related papers  (2020-03-22T00:50:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.