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31st ACM Web Conference (WWW)

作者： Medya, Sourav Rasoolinejad, Mohammad Yang, Yang Uzzi, Brian Northwestern Univ Kellogg Sch Management Evanston IL 60208 USA Signature Consultants San Francisco CA USA Syracuse Univ Sch Informat Studies Syracuse NY 13244 USA

ISBN: (纸本)9781450391306

Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical "hard" data reported in P&L statements or earnings per share reports. Yet, it is well-known that decision-makers routinely use "soft" text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. first, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.

关键词： Earnings call stock price movement natural language processing

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Semi-supervised Word Sense Disambiguation Using Example Similarity graph 14

Semi-supervised Word Sense Disambiguation Using Example Simi...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Yatabe, Rie Sasaki, Minoru Ibaraki University Japan

ISBN: (纸本)9781952148422

Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural networks and machine learning techniques to solve WSD problems. However, these previous supervised approaches often suffer from the lack of manually sense-tagged examples. In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples. The results of the experiments show that the proposed method performs better than the previous semi-supervised WSD method. Moreover, the graph structure between examples is effective for WSD and it is effective to utilize a graph structure obtained by fine-tuning BERT in the proposed method. © COLING *** rights reserved.

关键词： natural language processing systems

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Recognition algorithm for cross-texting in text chat conversations

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DATA & KNOWLEDGE ENGINEERING 2024年 150卷

作者： Lee, Da-Young Cho, Hwan-Gue Pusan Natl Univ Dept Informat Convergence Engn Pusan South Korea

As the development of the Internet and IT technology, short-text based communication is so popular compared with voice based one. Chat-based communication enables rapid, short and massive exchange of message with many people, creates new social problems. 'Cross-texting' is one of them. It refers to accidentally sending a text to an unintended person during the concurrent conversations with separated multiple people. Cross-texting would be a serious problem in languages where respectful expressions are required. As text-based communication is getting popular, it is a crucial work to prevent cross-texting by detecting it in advance in languages with honorifics expression such as Korean. In this paper, we proposed two methods detecting a cross-text using a deep learning model. The first model is the formal feature vector, which models dialog by explicitly defining the politeness and completeness features. The second one is the grpah2vec based ChatGram-net model, which models the dialog based on the syllable occurrence relationship. To evaluate the detection performance, we suggest a generating method for cross-text datasets from a actual messenger corpus. In experiment we show that both proposed models detected cross-text effectively, and exceeded the performance of the baseline models.

关键词： Text mining Mining methods and algorithms Cross-texting natural language processing Text graph embedding

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Point Cloud Geometry Coding with Relational Neighborhood Self-Attention

Point Cloud Geometry Coding with Relational Neighborhood Sel...

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IEEE workshop on Multimedia Signal processing

作者： Mohammadreza Ghafari André F. R. Guarda Nuno M. M. Rodrigues Fernando Pereira Instituto Superior Técnico - Universidade de Lisboa Instituto de Telecomunicações Lisbon Portugal Instituto de Telecomunicações Lisbon Portugal ESTG-Politécnico de Leiria Instituto de Telecomunicações Lisbon Portugal

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

In the ever-evolving landscape of deep learning, attention models have contributed to boost the performance in diverse fields such as computer vision and natural language processing. Following this trend, this paper proposes a novel Relational Neighborhood Self-Attention (RNSA) model, specifically designed for Point Cloud (PC) geometry coding to be integrated in the emerging learning-based JPEG PCC standard. The RNSA model proposes three new methods: first, to effectively learn correlations between the points by capturing the relational features and positions of neighboring points; second, to address the inefficiencies of conventional dot product attention, a novel Relational Scoring method to generate an attention map able to capture both linear and non-linear relationships between points and their neighbors is adopted; third, the created attention maps are normalized by Sparsemax instead of Softmax to generate sparse probabilities and assigns higher scores to the most important neighbors while marginalizing the less significant ones. Experimental results show that the proposed attention model achieves around 8% gains in both BD-Rate PSNR Dl and PSNR D2 compared to the baseline codec, i.e., JPEG PCC, while adding a small number of model parameters to JPEG PCC.

关键词： Point cloud compression Geometry Deep learning Computational modeling Transform coding Signal processing Market research Encoding natural language processing Standards

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LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking 59

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linki...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Jiang, Hang Gurajada, Sairam Lu, Qiuhao Neelam, Sumit Popa, Lucian Sen, Prithviraj Li, Yunyao Gray, Alexander MIT Cambridge MA 02139 USA IBM Res Yorktown Hts NY USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781954085527

Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNNEL, a neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches, with the added benefits of extensibility and transferability. In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. For instance, on the LC-QuAD-1.0 dataset, we show more than 4% increase in F1 score over previous SotA. Finally, we show that the inductive bias offered by using logic results in learned rules that transfer well across datasets, even without fine tuning, while maintaining high accuracy.

关键词： Heuristic methods

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A unified approach to sentence segmentation of punctuated text in many languages 59

A unified approach to sentence segmentation of punctuated te...

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作者： Wicks, Rachel Post, Matt Johns Hopkins Univ Ctr Language & Speech Proc Baltimore MD 21218 USA Johns Hopkins Univ Human Language Technol Ctr Excellence Baltimore MD 21218 USA

ISBN: (纸本)9781954085527

The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is sentence segmentation. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ERSATZ, as open source.

关键词： Text processing

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Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition 59

Locate and Label: A Two-stage Identifier for Nested Named En...

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作者： Shen, Yongliang Ma, Xinyin Tan, Zeqi Zhang, Shuai Wang, Wen Lu, Weiming Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781954085527

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. first we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

关键词： natural language processing systems

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graph-based text representation for novelty detection 1

Graph-based text representation for novelty detection

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1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Gamon, Michael Microsoft Research Redmond WA98052 United States

We discuss several feature sets for novelty detection at the sentence level, using the data and procedure established in task 2 of the TREC 2004 novelty track. In particular, we investigate feature sets derived from graph representations of sentences and sets of sentences. We show that a highly connected graph produced by using sentence-level term distances and pointwise mutual information can serve as a source to extract features for novelty detection. We compare several feature sets based on such a graph representation. These feature sets allow us to increase the accuracy of an initial novelty classifier which is based on a bag-of-word representation and KL divergence. The final result ties with the best system at TREC 2004. © 2006 Association for Computational Linguistics

关键词： graphic methods

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Text-space graph foundation models: comprehensive benchmarks and new insights 24

Text-space graph foundation models: comprehensive benchmarks...

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proceedings of the 38th International Conference on Neural Information processing Systems

作者： Zhikai Chen Haitao Mao Jingzhe Liu Yu Song Bingheng Li Wei Jin Bahare Fatemi Anton Tsitsulin Bryan Perozzi Hui Liu Jiliang Tang Michigan State University Emory University Google Research

ISBN: (纸本)9798331314385

Given the ubiquity of graph data and its applications in diverse domains, building a graph Foundation Model (GFM) that can work well across different graphs and tasks with a unified backbone has recently garnered significant interests. A major obstacle to achieving this goal stems from the fact that graphs from different domains often exhibit diverse node features. Inspired by multi-modal models that align different modalities with natural language, the text has recently been adopted to provide a unified feature space for diverse graphs. Despite the great potential of these text-space GFMs, current research in this field is hampered by two problems. first, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs. Second, there is a lack of sufficient datasets to thoroughly explore the methods' full potential and verify their effectiveness across diverse settings. To address these issues, we conduct a comprehensive benchmark providing novel text-space datasets and comprehensive evaluation under unified problem settings. Empirical results provide new insights and inspire future research directions. Our code and data are publicly available from https://***/CurryTang/TSGFM.

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CommitBERT: Commit Message Generation Using Pre-Trained Programming language Model 1

CommitBERT: Commit Message Generation Using Pre-Trained Prog...

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1st workshop on natural language processing for Programming, NLP4Prog 2021

作者： Jung, Tae-Hwan Kyung Hee University Korea Republic of

ISBN: (纸本)9781954085640

In version control using Git, the commit message is a document that summarizes source code changes in natural language. A good commit message clearly shows the source code changes, so this enhances collaboration between developers. To write a good commit message, the message should briefly summarize the source code changes, which takes a lot of time and effort. Therefore, a lot of research has been studied to automatically generate a commit message when a code modification is given. However, in most of the studies so far, there was no curated dataset for code modifications (additions and deletions) and corresponding commit messages in various programming languages. The model also had difficulty learning the contextual representation between code modification and natural language. To solve these problems, we propose the following two methods: (1) We collect code modification and corresponding commit messages in Github for six languages (Python, PHP, Go, Java, JavaScript, and Ruby) and release a well-organized 345K pair dataset. (2) In order to resolve the large gap in contextual representation between programming language (PL) and natural language (NL), we use CodeBERT (Feng et al., 2020), a pre-trained language model (PLM) for programming code, as an initial model. Using two methods leads to successful results in the commit message generation task. Also, this is the first research attempt in fine-tuning commit generation using various programming languages and code PLM. Training code, dataset, and pretrained weights are available at https://***/graykode/commitautosuggestions. © 2021 Association for Computational Linguistics.

关键词： Computational linguistics

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