Computational Linguistics, established in 1974, is the official flagship journal of the Association for Computational Linguistics (ACL). It is a leading venue for scholarly work spanning computational linguistics, natural language processing, and related areas in the computational study of language. The journal publishes high-quality, peer-reviewed research advancing our understanding of language through computation. It welcomes strong contributions in theoretical modeling, empirical investigation, system design, and scalable methods for language representation, generation, and understanding, with particular emphasis on challenging aspects of language modeling, emerging computational paradigms (including large-scale language models), and research bridging theoretical, empirical, linguistic, and interdisciplinary perspectives. To ensure timely, high-quality dissemination, Computational Linguistics maintains a rigorous yet efficient editorial process. As of 2025, the average time to first decision is 16.5 days (including desk rejections) and 55.0 days (excluding desk rejections). All articles are freely available online, published electronically by MIT Press (E-ISSN: 1530-9312) with immediate open access. Print issues (ISSN: 0891-2017) are released quarterly and distributed globally. Original papers may also be presented at ACL-sponsored conferences such as ACL and EMNLP, with select works highlighted during these events. The current Editor-in-Chief is Wei Lu (since 2024). Meet the full editorial team here. At a Glance More information about the journal, including its focus and scope, review process, and categories of submissions, can be found here. Detailed submission guidelines can be found here. Guidelines and information for special issue proposals can be found here. Highlights and Announcements Congratulations to Julia Hirschberg, former Editor-in-Chief of Computational Linguistics, on receiving the 2025 ACL Dragomir Radev Distinguished Service Award! Presentation at EMNLP 2026 - Deadlines and Dates Announced Presentation at ACL 2026 - Deadlines and Dates Announced Opening a New Chapter for Computational Linguistics (51:1) - An Editorial with a New Vision, at the Journal's 50th Anniversary, by the Editor-in-Chief, Wei Lu ACL Lifetime Achievement Award Highlight (51:1) - Ralph Grishman Special Issue (50:4) - Language Learning, Representation, and Processing in Human Machines
Large Language Model Instruction Following: A Survey of Progresses and Challenges
Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: First, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning to follow task instructions, that is, instruction following. Despite its impressive progress, there are some unsolved research equations that the community struggles with. This survey tries to summarize and provide insights into the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How should we model instructions? (iii) What are popular instruction following datasets and evaluation metrics? (iv) What factors influence and explain the instructions’ performance? (v) What challenges remain in instruction following? To our knowledge, this is the first comprehensive survey about instruction following.1
Copyright (c) 2024 Computational Linguistics
An Alternative Conception of Compositional Distributional Semantics
We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We show that these structures have the potential to capture the full sentential contexts of a lexeme and provide a uniform basis for the composition of distributional knowledge in a way that captures both mutual disambiguation and generalization.
Special Issue : Formal Distributional Semantics
Multiple Adjunction in Feature-based Tree-Adjoining Grammar
While independent derivations have been shown by Schabes and Shieber (1994) to be essential for correctly supporting syntactic analysis, semantic interpretation and statistical language modelling, the parsing algorithm they propose is restricted to TAG and is therefore not directly applicable to large scale implemented Feature-Based Lexicalised TAGs (FB-LTAG). We provide a recognition algorithm for FB-LTAG which supports both dependent and independent derivations under certain restrictions enforced jointly by feature constraints and by side conditions on the inference rules of the parsing algorithm. The resulting algorithm combines the benefits of independent derivations with those of Feature-Based grammars. In particular, we show that it accounts for a range of interactions between dependent vs. independent derivation on the one hand, and syntactic constraints, linear ordering, and scopal vs. non scopal semantic dependencies on the other hand.
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Languages may share universal features at a deep, abstract level, but the structures found in real-world, surface-level natural language vary significantly. This variation makes it challenging to transfer systems across languages or to develop Natural Language Processing (NLP) systems that apply to a wide range of languages. As a result, there is a vast geographical and linguistic disparity in the availability of NLP technology, with the majority of approaches developed for a handful of resource-rich languages, leaving many other languages behind. Understanding linguistic variation in a structured and systematic way is essential for the development of effective multilingual NLP applications and thus making language technology accessible to the wider world. The field of linguistic typology studies and classifies the world’s languages according to their structural and functional features, with the aim of explaining both the common properties and the structural diversity of languages. In NLP typological information has provided valuable guidance for multilingual tasks, as shown most clearly in the areas of morphosyntax and phonology. The investigated approaches include transfer from resource-rich to resource-poor languages (Padó and Lapata 2005; Khapra et al. 2011; Das and Petrov 2011; Täckström, McDonald, and Uszkoreit 2012), joint multilingual learning (Snyder 2010; Cohen, Das, and Smith 2011; Navigli and Ponzetto 2012), and development of universal models (de Marneffe et al. 2014; Nivre et al. 2016). Multilingual models can even outperform the best monolingual models (Ammar et al. 2016a; Tsvetkov et al. 2016; Adel, Vu, and Schultz 2013) by exploiting the systematic similarities and differences across languages. Such models can, in turn, help and inform research on linguistic typology itself, facilitating data-driven induction of typological knowledge. Despite this high potential of benefiting each other, the two fields mainly continue to develop independently, co-existing as two separate research communities. The goal of this paper is to provide a a comprehensive review of research at the intersection between multilingual NLP and linguistic typology. Offering an in-depth analysis of available typological information resources and approaches that integrate typological information in NLP, this review can not only encourage further advances in the two fields but can also help and build a bridge between the two communities for mutual benefit. We will considerably expand on the two existing short surveys of this area (Bender 2016; O’Horan et al. 2016), covering the field both in greater depth and breadth. We will provide a more detailed background on linguistic typology and multilingual NLP and will offer an extensive analysis of relevant methods, feature sets and their performance and limitations. We will also cover areas thus far neglected, most notably typological semantics, and provide an analysis of how typological constraints can be integrated in NLP methods from a machine learning perspective.
Rethinking Data Use in Large Language Models
Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this work, we discuss our research on understanding and advancing these models, centered around how they use the very large text corpora they are trained on. First, we describe our efforts to understand how these models learn to perform new tasks after training, demonstrating that their so-called in context learning capabilities are almost entirely determined by what they learn from the training data. Next, we introduce a new class of LMs—nonparametric LMs— that repurpose this training data as a data store from which they retrieve information for improved accuracy and updatability. We discuss our work establishing the foundations of such models, including one of the first broadly used neural retrieval models and an approach that simplifies a traditional, two-stage pipeline into one. We also discuss how nonparametric models open up new avenues for responsible data use, e.g., by segregating permissive and copyrighted text and using them differently. Finally, we envision the next generation of LMs we should build, focusing on efficient scaling, improved factuality, and decentralization.
Copyright (c) 2026 Computational Linguistics
Book Review Automated Essay Scoring Beata Beigman Klebanov and Nitin Madnani (Educational Testing Service) Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 52), 2022, xx+294 pp; paperback, ISBN 9781636392226; ebook, ISBN 9781636392233; hardcover, ISBN 9781636392240; doi:10.2200/S01121ED1V01Y202108HLT052 Reviewed by Ana¨ıs Tack KU Leuven
Copyright (c) 2025 Computational Linguistics
LFG-Generation from Acyclic F-Structures is NP-Hard
The universal generation problem for LFG grammars is the problem of determining whether a given grammar derives any terminal string with a given f-structure. It is known that this problem is decidable for acyclic f-structures. In this brief note, we show that for those f-structures the problem is nonetheless intractable. This holds even for grammars that are off-line parsable. Â
Large Linguistic Corpus Reduction with SCP Algorithms
For pratical reasons (mainly related to costs), and to meet the quality expectations of the associated application, corpus design is a crucial issue for building rich annotated text corpora. Reducing a large corpus while maintaining sufficient linguistic richness can be formalized as a Set Covering Problem (SCP). Within this context, we present in this paper two algorithmic heuristics applied to design large text corpora in English and French and covering multi-represented phonological units. The first considered algorithm is a standard greedy solution with an agglomerative/spitting strategy. We propose a second algorithm based on Lagrangian relaxation. This approach provides a lower bound concerning the cost of each covering solution. This lower bound can be used as a metric to evaluate the quality of a reduced corpus whatever the algorithm applied. Experiments show that a suboptimal algorithm like a Greedy achieves good results; the cost of its solutions is not so far from the lower bound (about 4.35% for the triphoneme coverings). Usually constraints on SCP are binary, we proposed here a generalization where the constraint on each covering feature can be multi-valued.
IDT: Dual-Task Adversarial Rewriting for Attribute Anonymisation
Natural language processing (NLP) models may leak private information in different ways, including membership inference, reconstruction, or attribute inference attacks. Sensitive information may not be explicit in the text, but hidden in underlying writing characteristics. Methods to protect privacy can involve using representations inside models that are demonstrated not to detect sensitive attributes or—for instance, in cases where users might be at risk from an untrustworthy model, the sort of scenario of interest here—changing the raw text before models can have access to it. The goal is to rewrite text to prevent someone from inferring a sensitive attribute (e.g., the gender of the author, or their location by the writing style) while keeping the text useful for its original intention (e.g., the sentiment of a product review). The few works tackling this have focused on generative techniques. However, these often create extensively different texts from the original ones or face problems such as mode collapse. This article explores a novel adaptation of adversarial attack techniques to manipulate a text to deceive a classifier w.r.t. one task (privacy) while keeping the predictions of another classifier trained for another task (utility) unchanged. We propose IDT, a method that analyses predictions made by auxiliary and interpretable models to identify which tokens are important to change for the privacy task, and which ones should be kept for the utility task. We evaluate different datasets for NLP suitable for different tasks. Automatic and human evaluations show that IDT retains the utility of text, while also outperforming existing methods when deceiving a classifier w.r.t. a privacy task.
Tokenization as Finite-State Transduction
Tokenization is the first step in modern neural language model pipelines where an input text is converted to a sequence of subword tokens. We introduce from first principles a finite-state transduction framework which can encode all possible tokenizations of a regular language. We then constructively show that Byte-Pair Encoding (BPE) and MaxMatch (WordPiece), two popular tokenization schemes, are also efficiently representable by simple finite-state transducers. For BPE, this is particularly surprising given that it does not tokenize strings from left to right and requires a notion of priority. We also discuss an application of subword-level pattern promotion to guided generation, where the outputs of a language model are constrained to match a specified pattern, and how tokenization-aware promotion offers a theoretical benefit to modeling.
The ACL community has very little interest in evaluating the real-world impact of NLP systems. A structured survey of the ACL Anthology shows that perhaps 0.1% of its papers contain such evaluations; furthermore most papers which include impact evaluations present them very sketchily and instead focus on metric evaluations. NLP technology would be more useful and more quickly adopted if we seriously tried to understand and evaluate its real-world impact.
Editorial: Opening a New Chapter for Computational Linguistics
By the end of 2024, the journal Computational Linguistics has reached a significant milestone: It has published exactly 50 volumes over the past half-century. As we launch the first issue of Volume 51, this is an opportune moment to reflect on the journal’s legacy, ongoing evolution, and the exciting changes that lie ahead. Together, we embark on a journey to open a new chapter for this storied publication.
Book Review: Automatic Language Identification in Texts
Book Review: Automatic Language Identification in Texts Tommi Jauhiainen, Marcos Zampieri, Timothy Baldwin, and Krister Lind´en University of Helsinki, George Mason University, MBZUAI, and University of Helsinki Springer (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 44), 2024, xiv+148 pp; ISBN 978-3-031-45821-7; ebook, ISBN 978-3-031-45822-4; doi:10.1007/978-3-031-45822-4 Reviewed by Tom Lippincott Johns Hopkins University
MUCking In, or Fifty Years in Information Extraction
I want to thank the ACL for this Lifetime Achievement Award. I am deeply honored to be receiving it. I would also like to thank the students, faculty, and researchers who were members of the Proteus Project during most of my professional lifetime. It was an honor to serve that group.
Survey of Cultural Awareness in Language Models: Text and Beyond
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive, going beyond multilinguality and building on findings from psychology and anthropology. In this paper, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking definitions of culture from the anthropology and psychology literature as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of Human-Computer Interaction (HCI) in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.
LGDE: Local Graph-based Dictionary Expansion
We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings to capture word similarities based on paths of semantic association, over and above direct pairwise similarities. Exploiting such semantic neighbourhoods enables the expansion of dictionaries of pre-selected keywords, an important step for tasks in information retrieval, such as database queries and online data collection. We validate LGDE on a corpus of English-language hate speech-related posts from Reddit and Gab and show that LGDE enriches the list of keywords with significantly better performance than threshold methods based on direct word similarities. We further demonstrate our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on the expansion of a conspiracy-related dictionary from online data collected and analysed by domain experts. Our empirical results and expert user assessment indicate that LGDE expands the seed dictionary with more useful keywords due to the manifold-learning-based similarity network.
Socially Aware Language Technologies: Perspectives and Practices
Language technologies have advanced substantially, particularly with the introduction of large language models. However, these advancements can exacerbate several issues that models have traditionally faced, including bias, evaluation, and risk. In this perspective paper, we argue that many of these issues share a common core: a lack of awareness of the social factors, interactions, and implications of the social environment in which NLP operates. We call this social awareness. While NLP is improving at addressing linguistic issues, there has been relatively limited progress in incorporating social awareness into models to work in all situations for all users. Integrating social awareness into NLP will improve the naturalness, usefulness, and safety of applications while also opening up new applications. Today, we are only at the start of a new, important era in the field.
Large Language Models Are Biased Because They Are Large Language Models
This position paper’s primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.
Measuring Grammatical Diversity from Small Corpora: Derivational Entropy Rates, Mean Length of Utterances, and Annotation Invariance
In many fields, such as language acquisition, neuropsychology of language, the study of aging, and historical linguistics, corpora are used for estimating the diversity of grammatical structures that are produced during a period by an individual, community, or type of speakers. In these cases, treebanks are taken as representative samples of the syntactic structures that might be encountered. Generalizing the potential syntactic diversity from the structures documented in a small corpus requires careful extrapolation whose accuracy is constrained by the limited size of representative sub-corpora. In this article, I demonstrate --theoretically, and empirically-- that a grammar's derivational entropy and the mean length of the utterances (MLU) it generates are fundamentally linked, giving rise to a new measure, the derivational entropy rate. The mean length of utterances becomes the most practical index of syntactic complexity; I demonstrate that MLU is not a mere proxy, but a fundamental measure of syntactic diversity. In combination with the new derivational entropy rate measure, it provides a theory-free assessment of grammatical complexity. The derivational entropy rate indexes the rate at which different grammatical annotation frameworks determine the grammatical complexity of treebanks. I evaluate the Smoothed Induced Treebank Entropy (SITE) as a tool for estimating these measures accurately, even from very small treebanks. I conclude by discussing important implications of these results for both NLP and human language processing.
Parsing Argumentation Structures in Persuasive Essays
In this article, we present the first end-to-end approach for parsing argumentation structures in persuasive essays. We model the argumentation structure as a tree including several types of argument components connected with argumentative support and attack relations. We consider the identification of argumentation structures in several consecutive steps. First, we segment a persuasive essay in order to identify relevant argument components. Second, we jointly model the classification of argument components and the identification of argumentative relations using Integer Linear Programming. Third, we recognize the stance of each argument component in order to discriminate between argumentative support and attack relations. By evaluating the joint model using two corpora, we show that our approach not only considerably improves the identification of argument component types and argumentative relations but also significantly outperforms a challenging heuristic baseline. In addition, we introduce a novel corpus including 402 persuasive essays annotated with argumentation structures and show that our new annotation guideline successfully guides annotators to substantial agreement.
Problem Solving Through Human-AI Preference-Based Cooperation
While there is a widespread belief that artificial general intelligence (AGI) – or even superhuman AI – is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including inability to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human-AI co-construction framework. We formalize HAI-Co2 and discuss the difficult open research problems that it faces. Finally, we present a case study of HAI-Co2 and demonstrate its efficacy compared to monolithic generative AI models.
Language Learning, Representation, and Processing in Humans and Machines: Introduction to the Special Issue
Large Language Models (LLMs) and humans acquire knowledge about language without direct supervision. LLMs do so by means of specific training objectives, while humans rely on sensory experience and social interaction. This parallelism has created a feeling in NLP and cognitive science that a systematic understanding of how LLMs acquire and use the encoded knowledge could provide useful insights for studying human cognition. Conversely, methods and findings from the field of cognitive science have occasionally inspired language model development. Yet, the differences in the way that language is processed by machines and humans—in terms of learning mechanisms, amounts of data used, grounding and access to different modalities—make a direct translation of insights challenging. The aim of this edited volume has been to create a forum of exchange and debate along this line of research, inviting contributions that further elucidate similarities and differences between humans and LLMs.
Special Issue on Language Learning, Representation, and Processing in Humans and Machines
Natural Language Processing RELIES on Linguistics
Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence. What does this mean for the future of linguistic expertise in NLP? We highlight several aspects in which NLP (still) relies on linguistics, or where linguistic thinking can illuminate new directions. We argue our case around the acronym RELIES, which encapsulates six major facets where linguistics contributes to NLP: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and the Study of language. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes; but at a macro level, these facets highlight the enduring importance of studying machine systems vis-à-vis systems of human language.
Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics
We investigate two essential challenges in the context of Hierarchical Topic Modeling (HTM) -- (i) the impact of data representation and (ii) topic evaluation. The \textit{Data representation} directly influences the performance of the topic generation, and the impact of new representations such as contextual embeddings in this task has been under-investigated. Topic evaluation, responsible for driving the advances in the field, assesses the overall quality of the topic generation process. HTM studies exploit the exact topic modeling (TM) evaluation metrics as traditional TM to measure the quality of topics. One significant result of our work is demonstrating that the HTM's hierarchical nature demands novel ways of evaluating the quality of topics. As our main contribution, we propose two new topic quality metrics to assess the topical quality of the hierarchical structures. Uniqueness considers topic topological consistency, while the Semantic Hierarchical Structure (SHS) captures the semantic relatedness of the hierarchies. We also present an additional advance to the state-of-the-art by proposing the c-CluHTM. To the best of our knowledge, c-CluHTM is the first method that exploits contextual embeddings into NMF in HTM tasks. c-CluHTM enhances the topics' semantics while preserving the hierarchical structure. We perform an experimental evaluation, and our results demonstrate the superiority of our proposal with gains between 12% and 21%, regarding NPMI and Coherence over the best baselines. Regarding the newly proposed metrics, our results reveal that Uniqueness and SHS can capture relevant information about the structure of the hierarchical topics that traditional metrics cannot.
LLM-based NLG Evaluation: Current Status and Challenges
Evaluating natural language generation (NLG) is a vital but challenging problem in natural language processing. Traditional evaluation metrics mainly capturing content (e.g., n-gram) overlap between system outputs and references are far from satisfactory, and large language models (LLMs) such as ChatGPT have demonstrated great potential in NLG evaluation in recent years. Various automatic evaluation methods based on LLMs have been proposed, including metrics derived from LLMs, prompting LLMs, fine-tuning LLMs, and human–LLM collaborative evaluation. In this survey, we first give a taxonomy of LLM-based NLG evaluation methods, and discuss their pros and cons, respectively. Lastly, we discuss several open problems in this area and point out future research directions.
LMLPA: Language Model Linguistic Personality Assessment
Large Language Models (LLMs) are increasingly used in everyday life and research. One of the most common use cases is conversational interactions, enabled by the language generation capabilities of LLMs. Just as between two humans, a conversation between an LLM-powered entity and a human depends on the personality of the conversants. However, measuring the personality of a given LLM is currently a challenge. This article introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs. Our system helps to understand LLMs' language generation capabilities by quantitatively assessing the distinct personality traits reflected in their linguistic outputs. Unlike traditional human-centric psychometrics, the LMLPA adapts a personality assessment questionnaire, specifically the Big Five Inventory, to align with the operational capabilities of LLMs, and also incorporates the findings from previous language-based personality measurement literature. To mitigate sensitivity to the order of options, our questionnaire is designed to be open-ended, resulting in textual answers. Thus, the Artificial Intelligence (AI) rater is needed to transform ambiguous personality information from text responses into clear numerical indicators of personality traits. Utilising Principal Component Analysis and reliability validation methods, our findings demonstrate that LLMs possess distinct personality traits that can be effectively quantified by the LMLPA. This research contributes to Human-Centered AI and Computational Linguistics, providing a robust framework for future studies to refine AI personality assessments and expand their applications in multiple areas, including education and manufacturing
Kallini et al. (2024) do not compare impossible languages with constituency-based ones
A central goal of linguistic theory is to find a precise characterization of the notion "possible human language", in the form of a computational device that is capable of describing all and only the languages that can be acquired by a typically developing human child. The success of recent large language models (LLMs) in NLP applications arguably raises the possibility that LLMs might be computational devices that meet this goal. This would only be the case if, in addition to succeeding in learning human languages, LLMs struggle to learn "impossible" human languages. Kallini et al. (2024; "Mission: Impossible Language Models", Proc. ACL) conducted experiments aiming to test this by training GPT-2 on a variety of synthetic languages, and found that it learns some more successfully than others. They present these asymmetries as support for the idea that LLMs' inductive biases align with what is regarded as "possible" for human languages, but the most significant comparison has a confound that makes this conclusion unwarranted. In this paper I explain the confound and suggest some ways forward towards constructing a comparison that appropriately tests the underlying issue.
Understanding Dialogue: Language Use and Social Interaction
Understanding Dialogue: Language Use and Social Interaction Martin J. Pickering and Simon Garrod (University of Edinburgh & University of Glasgow) Cambridge, England: Cambridge University Press, 2021, xvii+283 pp; hardback, ISBN 978-1-108-47361-3; paperback, ISBN 978-1-108-46193-1 Reviewed by Rachel Bawden Inria, France
Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning
Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning by Mohammad Taher Pilehvar and Jose Camacho-Collados (Tehran Institute for Advanced Studies & Cardiff University). Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 47), 2021, xvii+157 pp; paperback, ISBN 978-1-63639-021-5; ebook, ISBN 978-1-63639-022-2; hardcover, ISBN 978-1-63639-023-9; doi:10.2200/S01057ED1V01Y202009HLT047
Natural Language Processing andComputational Linguistics
Lifetime Achievement Award Natural Language Processing and Computational Linguistics Junichi Tsujii Artificial Intelligence Research Center National Institute for Advanced Industrial Science and Technology, Japan Department of Computer Science University of Manchester, UK j-tsujii@aist.go.jp