A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker\'s intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.

A central topic in psycholinguistics is the study of how and when the parser assigns an antecedent to referentially-dependent elements. One such referentially-dependent element is the null subject of non-finite clauses. The aim of the present study was to examine the role of verb control information in the assignment of an antecedent to such a null subject. The results so far are inconclusive. Some authors argue that verb control information has a late influence, whereas others argue that such verb-specific information has a very rapid influence. We report a self-paced reading study in Spanish in which verb type (subject vs. object control) and grammaticality (grammatical vs. ungrammatical) were manipulated. The grammaticality manipulation was carried out by introducing a person anomaly at the infinitive itself, and not at a later word (e.g., \"Te prometí/aconsejé adelgazarme/adelgazarte cinco quilos en un mes.\" Literal translation, \"I to you promised/advised to losemyself/yourself five kilos in a month\"). With such a manipulation we can examine whether at the first possible point (i.e., the infinitive) verb control information was used to assign the correct antecedent (i.e., the subject in sentences with a subject-control verb, and the object in sentences with an object-control verb) to PRO. The results showed that at the infinitive there was a main effect of grammaticality, meaning that the correct antecedent has already been assigned to PRO. The present findings are consistent with models that assume that verb-specific information plays an important role in the initial stages of sentence processing.

Music can be interpreted by attributing syntactic relationships to sequential musical events, and, computationally, such musical interpretation represents an analogous combinatorial task to syntactic processing in language. While this perspective has been primarily addressed in the domain of harmony, we focus here on rhythm in the Western tonal idiom, and we propose for the first time a framework for modeling the moment-by-moment execution of processing operations involved in the interpretation of music. Our approach is based on (1) a music-theoretically motivated grammar formalizing the competence of rhythmic interpretation in terms of three basic types of dependency (preparation, syncopation, and split; Rohrmeier, 2020), and (2) psychologically plausible predictions about the complexity of structural integration and memory storage operations, necessary for parsing hierarchical dependencies, derived from the dependency locality theory (Gibson, 2000). With a behavioral experiment, we exemplify an empirical implementation of the proposed theoretical framework. One hundred listeners were asked to reproduce the location of a visual flash presented while listening to three rhythmic excerpts, each exemplifying a different interpretation under the formal grammar. The hypothesized execution of syntactic-processing operations was found to be a significant predictor of the observed displacement between the reported and the objective location of the flashes. Overall, this study presents a theoretical approach and a first empirical proof-of-concept for modeling the cognitive process resulting in such interpretation as a form of syntactic parsing with algorithmic similarities to its linguistic counterpart. Results from the present small-scale experiment should not be read as a final test of the theory, but they are consistent with the theoretical predictions after controlling for several possible confounding factors and may form the basis for further large-scale and ecological testing.

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400-like negativity when the disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents.

To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad-coverage tools from natural-language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context-free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next-word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure-building predominantly in the left posterior temporal lobe: CCG-derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure-building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds.

In online language comprehension, the parser incrementally builds hierarchical syntactic structures. The predictive nature of this structure-building process has been the subject of extensive debate. A previous study observed that when a wh-phrase indicates parallelism between the upcoming wh-clause and a preceding clause (e.g., John told some stories, but we couldn\'t remember which stories…), the parser predictively constructs the wh-clause. This observation demonstrates predictive structure building. However, the study also suggests that the parser does not make a prediction when the wh-phrase indicates that parallelism does not hold (e.g., John told some stories … with which stories…), a potential limit to the prediction of syntactic structures. Crucially, these findings are controversial because the study did not observe processing difficulty when disambiguating input indicated that the predicted continuation was inconsistent with the globally grammatical structure (garden-path effects). The controversial results may be due to a lack of statistical power. Therefore, the present study conducted a large-scale replication study (324 participants and 24 sets of materials). The results revealed that the parser predicts the clausal structure, irrespective of the type of wh-phrase. There was also evidence of garden-path effects, supporting the finding that the parser makes a prediction. These observations suggest that the prediction algorithm inherent in the human parser is more powerful than assumed by the previous study and that the parser attempts to construct globally grammatical structures during revision.

Proper identification of collocations (and more generally of multiword expressions (MWEs), is an important qualitative step for several NLP applications and particularly so for translation. Since many MWEs cannot be translated literally, failure to identify them yields at best inaccurate translation. This paper is mostly be concerned with collocations. We will show how they differ from other types of MWEs and how they can be successfully parsed and translated by means of a grammar-based parser and translator.

PubMed is an invaluable resource for the biomedical community. Although PubMed is freely available, the existing API is not designed for large-scale analyses and the XML structure of the underlying data is inconvenient for complex queries. We developed an R package called pmparser to convert the data in PubMed to a relational database. Our implementation of the database, called PMDB, currently contains data on over 31 million PubMed Identifiers (PMIDs) and is updated regularly. Together, pmparser and PMDB can enable large-scale, reproducible, and transparent analyses of the biomedical literature. pmparser is licensed under GPL-2 and available at https://pmparser.hugheylab.org. PMDB is available in both PostgreSQL (DOI 10.5281/zenodo.4008109) and Google BigQuery (https://console.cloud.google.com/bigquery?project=pmdb-bq&d=pmdb).

BACKGROUND:  : Syntactic analysis, or parsing, is a key task in natural language processing and a required component for many text mining approaches. In recent years, Universal Dependencies (UD) has emerged as the leading formalism for dependency parsing. While a number of recent tasks centering on UD have substantially advanced the state of the art in multilingual parsing, there has been only little study of parsing texts from specialized domains such as biomedicine.
METHODS:  : We explore the application of state-of-the-art neural dependency parsing methods to biomedical text using the recently introduced CRAFT-SA shared task dataset. The CRAFT-SA task broadly follows the UD representation and recent UD task conventions, allowing us to fine-tune the UD-compatible Turku Neural Parser and UDify neural parsers to the task. We further evaluate the effect of transfer learning using a broad selection of BERT models, including several models pre-trained specifically for biomedical text processing.
RESULTS:  : We find that recently introduced neural parsing technology is capable of generating highly accurate analyses of biomedical text, substantially improving on the best performance reported in the original CRAFT-SA shared task. We also find that initialization using a deep transfer learning model pre-trained on in-domain texts is key to maximizing the performance of the parsing methods.

For a genome-wide association study in humans, genotype imputation is an essential analysis tool for improving association mapping power. When IMPUTE software is used for imputation analysis, an imputation output (GEN format) should be converted to variant call format (VCF) with imputed genotype dosage for association analysis. However, the conversion requires multiple software packages in a pipeline with a large amount of processing time.
We developed GEN2VCF, a fast and convenient GEN format to VCF conversion tool with dosage support.
The performance of GEN2VCF was compared to BCFtools, QCTOOL, and Oncofunco. The test data set was a 1 Mb GEN-formatted file of 5000 samples. To determine the performance of various sample sizes, tests were performed from 1000 to 5000 samples with a step size of 1000. Runtime and memory usage were used as performance measures.
GEN2VCF showed drastically increased performances with respect to runtime and memory usage. Runtime and memory usage of GEN2VCF was at least 1.4- and 7.4-fold lower compared to other methods, respectively.
GEN2VCF provides users with efficient conversion from GEN format to VCF with the best-guessed genotype, genotype posterior probabilities, and genotype dosage, as well as great flexibility in implementation with other software packages in a pipeline.