The Linear Arrangement Library. A new tool for research on syntactic dependency structures

• URL: http://arxiv.org/abs/2112.02512v1
• Date: Sun, 5 Dec 2021 08:48:52 GMT
• Title: The Linear Arrangement Library. A new tool for research on syntactic dependency structures
• Authors: Llu\'is Alemany-Puig and Juan Luis Esteban and Ramon Ferrer-i-Cancho
• Abstract summary: We present a new open-source tool, the Linear Arrangement Library (LAL) LAL caters to the needs of, especially, inexperienced programmers. It enables the calculation of metrics on single syntactic dependency structures, treebanks, and collection of treebanks.
• Score: 1.611401281366893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The new and growing field of Quantitative Dependency Syntax has emerged at the crossroads between Dependency Syntax and Quantitative Linguistics. One of the main concerns in this field is the statistical patterns of syntactic dependency structures. These structures, grouped in treebanks, are the source for statistical analyses in these and related areas; dozens of scores devised over the years are the tools of a new industry to search for patterns and perform other sorts of analyses. The plethora of such metrics and their increasing complexity require sharing the source code of the programs used to perform such analyses. However, such code is not often shared with the scientific community or is tested following unknown standards. Here we present a new open-source tool, the Linear Arrangement Library (LAL), which caters to the needs of, especially, inexperienced programmers. This tool enables the calculation of these metrics on single syntactic dependency structures, treebanks, and collection of treebanks, grounded on ease of use and yet with great flexibility. LAL has been designed to be efficient, easy to use (while satisfying the needs of all levels of programming expertise), reliable (thanks to thorough testing), and to unite research from different traditions, geographic areas, and research fields.

Related papers

• CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks [12.465309397733249]
  Large language models (LLMs) have been widely adopted across diverse software engineering domains.<n>These applications require understanding beyond surface-level code patterns.<n>Existing benchmarks primarily evaluate end-to-end outcomes, such as whether code is correctly repaired or generated.
  arXiv  Detail & Related papers  (2025-07-03T01:35:58Z)
• Is Compression Really Linear with Code Intelligence? [60.123628177110206]
  textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
  arXiv  Detail & Related papers  (2025-05-16T16:59:14Z)
• BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models [50.17907898478795]
  We introduce BinMetric, a benchmark designed to evaluate the performance of large language models on binary analysis tasks.<n>BinMetric comprises 1,000 questions derived from 20 real-world open-source projects across 6 practical binary analysis tasks.<n>Our empirical study on this benchmark investigates the binary analysis capabilities of various state-of-the-art LLMs, revealing their strengths and limitations in this field.
  arXiv  Detail & Related papers  (2025-05-12T08:54:07Z)
• FamilyTool: A Multi-hop Personalized Tool Use Benchmark [94.1158032740113]
  We introduce FamilyTool, a novel benchmark grounded in a family-based knowledge graph (KG) FamilyTool challenges Large Language Models with queries spanning 1 to 3 relational hops. Experiments reveal significant performance gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-04-09T10:42:36Z)
• Beyond Next Word Prediction: Developing Comprehensive Evaluation Frameworks for measuring LLM performance on real world applications [3.686808512438363]
  Large Language Models (LLMs) have numerous use-cases, and have already acquired a significant degree of enterprise adoption. This paper provides the basis for a more comprehensive evaluation framework, based upon a traditional game and tool-based architecture.
  arXiv  Detail & Related papers  (2025-03-05T06:44:38Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• A Software Engineering Perspective on Testing Large Language Models: Research, Practice, Tools and Benchmarks [2.8061460833143346]
  Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high-stake or safety-critical systems of 2030, they need to undergo rigorous testing.
  arXiv  Detail & Related papers  (2024-06-12T13:45:45Z)
• ComplexityMeasures.jl: scalable software to unify and accelerate entropy and complexity timeseries analysis [0.0]
  ComplexityMeasures.jl is an easily extendable and highly performant open-source software that implements a vast selection of complexity measures. The software provides 1638 measures with 3,841 lines of source code, averaging only 2.3 lines of code per exported quantity.
  arXiv  Detail & Related papers  (2024-06-07T15:22:45Z)
• STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases [93.96463520716759]
  We develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Knowledge Bases. Our benchmark covers three domains: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties.
  arXiv  Detail & Related papers  (2024-04-19T22:54:54Z)
• Advancing Code Coverage: Incorporating Program Analysis with Large Language Models [8.31978033489419]
  We propose TELPA, a novel technique to generate tests that can reach hard-to-cover branches.<n>Our experimental results on 27 open-source Python projects demonstrate that TELPA significantly outperforms the state-of-the-art SBST and LLM-based techniques.
  arXiv  Detail & Related papers  (2024-04-07T14:08:28Z)
• generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation [28.715001906405362]
  Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. We tackle this shortcoming by proposing a tree-in-the-loop approach, where a visual representation of the beam search tree is the central component for analyzing, explaining, and adapting the generated outputs. We present generAItor, a visual analytics technique, augmenting the central beam search tree with various task-specific widgets, providing targeted visualizations and interaction possibilities.
  arXiv  Detail & Related papers  (2024-03-12T13:09:15Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML) [0.0]
  The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus. We present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents.
  arXiv  Detail & Related papers  (2023-01-20T13:12:35Z)
• Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
  We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
  arXiv  Detail & Related papers  (2021-06-18T15:08:47Z)
• Comparative Code Structure Analysis using Deep Learning for Performance Prediction [18.226950022938954]
  This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. Our evaluations of several deep embedding learning methods demonstrate that tree-based Long Short-Term Memory (LSTM) models can leverage the hierarchical structure of source-code to discover latent representations and achieve up to 84% (individual problem) and 73% (combined dataset with multiple of problems) accuracy in predicting the change in performance.
  arXiv  Detail & Related papers  (2021-02-12T16:59:12Z)
• Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships [1.8352113484137629]
  We propose a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association. The goal is to use these word association models to reinforce current relation extraction approaches.
  arXiv  Detail & Related papers  (2020-04-29T15:25:28Z)

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.