The papers in this special issue reflect the increased status on lexical representations in sentence processing research.

The 18 contributions in Lexical Representation and Process provide a coherent and well-documented frame of reference for a field of study that is becoming central to both linguistics and psycholinguistics.

This book includes the work of experts from a wide range of backgrounds who share the desire to understand how the human brain represents words. The focus of the volume is on the nature and structure of word forms and morphemes, the processes operating on the speech input to gain access to lexical representations, the modeling and acquisition of these processes, and on the neural underpinnings of lexical representation and process.

Our ability to speak, write, understand speech and read is critical to our ability to function in today's society. As such, psycholinguistics, or the study of how humans learn and use language, is a central topic in cognitive science. This comprehensive handbook is a collection of chapters written not by practitioners in the field, who can summarize the work going on around them, but by trailblazers from a wide array of subfields, who have been shaping the field of psycholinguistics over the last decade. Some topics discussed include how children learn language, how average adults understand and produce language, how language is represented in the brain, how brain-damaged individuals perform in terms of their language abilities and computer-based models of language and meaning. This is required reading for advanced researchers, graduate students and upper-level undergraduates who are interested in the recent developments and the future of psycholinguistics.

The most frequently used words in English are highly ambiguous; for example, Webster's Ninth New Collegiate Dictionary lists 94 meanings for the word "run" as a verb alone. Yet people rarely notice this ambiguity. Solving this puzzle has commanded the efforts of cognitive scientists for many years. The solution most often identified is "context": we use the context of utterance to determine the proper meanings of words and sentences. The problem then becomes specifying the nature of context and how it interacts with the rest of an understanding system. The difficulty becomes especially apparent in the attempt to write a computer program to understand natural language. Lexical ambiguity resolution (LAR), then, is one of the central problems in natural language and computational semantics research. A collection of the best research on LAR available, this volume offers eighteen original papers by leading scientists. Part I, Computer Models, describes nine attempts to discover the processes necessary for disambiguation by implementing programs to do the job. Part II, Empirical Studies, goes into the laboratory setting to examine the nature of the human disambiguation mechanism and the structure of ambiguity itself. A primary goal of this volume is to propose a cognitive science perspective arising out of the conjunction of work and approaches from neuropsychology, psycholinguistics, and artificial intelligence--thereby encouraging a closer cooperation and collaboration among these fields. Lexical Ambiguity Resolution is a valuable and accessible source book for students and cognitive scientists in AI, psycholinguistics, neuropsychology, or theoretical linguistics.

Bilingual Sentence Processing

Investigations into employing statistical approaches with linguistically motivated representations and its impact on Natural Language processing tasks. The last decade has seen computational implementations of large hand-crafted natural language grammars in formal frameworks such as Tree-Adjoining Grammar (TAG), Combinatory Categorical Grammar (CCG), Head-driven Phrase Structure Grammar (HPSG), and Lexical Functional Grammar (LFG). Grammars in these frameworks typically associate linguistically motivated rich descriptions (Supertags) with words. With the availability of parse-annotated corpora, grammars in the TAG and CCG frameworks have also been automatically extracted while maintaining the linguistic relevance of the extracted Supertags. In these frameworks, Supertags are designed so that complex linguistic constraints are localized to operate within the domain of those descriptions. While this localization increases local ambiguity, the process of disambiguation (Supertagging) provides a unique way of combining linguistic and statistical information. This volume investigates the theme of employing statistical approaches with linguistically motivated representations and its impact on Natural Language Processing tasks. In particular, the contributors describe research in which words are associated with Supertags that are the primitives of different grammar formalisms including Lexicalized Tree-Adjoining Grammar (LTAG). Contributors Jens Bäcker, Srinivas Bangalore, Akshar Bharati, Pierre Boullier, Tomas By, John Chen, Stephen Clark, Berthold Crysmann, James R. Curran, Kilian Foth, Robert Frank, Karin Harbusch, Sasa Hasan, Aravind Joshi, Vincenzo Lombardo, Takuya Matsuzaki, Alessandro Mazzei, Wolfgang Menzel, Yusuke Miyao, Richard Moot, Alexis Nasr, Günter Neumann, Martha Palmer, Owen Rambow, Rajeev Sangal, Anoop Sarkar, Giorgio Satta, Libin Shen, Patrick Sturt, Jun'ichi Tsujii, K. Vijay-Shanker, Wen Wang, Fei Xia

Lexical Processing and Second Language Acquisition provides a comprehensive overview of research on second language lexical processing, integrating converging research and perspectives from Cognitive Science and Second Language Acquisition. The book begins by introducing the dominant issues addressed by research in the field in cognitive science and discussing the relevant models in the literature. It later moves toward exploring the different factors that impact second language lexical processing as well as cognitive neuroscientific approaches to the study of the issues discussed throughout the book. A concluding chapter offers a global summary of the key issues and research strands, in addition to directions for future research, with a list of recommended readings providing students and researchers with avenues for further study.

One of the liveliest forums for sharing psychological, linguistic, philosophical, and computer science perspectives on psycholinguistics has been the annual meeting of the CUNY Sentence Processing Conference. Documenting the state of the art in several important approaches to sentence processing, this volume consists of selected papers that had been presented at the Sixth CUNY Conference. The editors not only present the main themes that ran through the conference but also honor the breadth of the presentations from disciplines including linguistics, experimental psychology, and computer science. The variety of sentence processing topics examined includes: * how evoked brain potentials reflect sentence comprehension * how auditory words are processed * how various sources of grammatical and nongrammatical information are coordinated and used * how sentence processing and language acquisition might be related. This distinctive volume not only presents the most exciting current work in sentence processing, but also places this research into the broader context of theorizing about it.

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.