Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16

Some shortcomings of long-term working memory

Within the framework of their long-term working memory theory, Ericsson and Kintsch (1995) propose that experts rapidly store information in long-term memory through two mechanisms: elaboration of long-term memory patterns and schemas and use of retrieval structures. They use chess players’ memory as one of their most compelling sources of empirical evidence. In this paper, I show that evidence from chess memory, far from supporting their theory, limits its generality. Evidence from other domains reviewed by Ericsson and Kintsch, such as medical expertise, is not as strong as claimed, and sometimes contradicts the theory outright. I argue that Ericsson and Kintsch’s concept of retrieval structure conflates three different types of memory structures that possess quite different properties. One of these types of structures—generic, general-purpose retrieval structures—has a narrower use than proposed by Ericsson and Kintsch: it applies only in domains where there is a conscious, deliberate intent by individuals to improve their memory. Other mechanisms, including specific retrieval structures, exist that permit a rapid encoding into long-term memory under other circumstances

Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation

We argue for a performance-based design of natural language grammars and their associated parsers in order to meet the constraints imposed by real-world NLP. Our approach incorporates declarative and procedural knowledge about language and language use within an object-oriented specification framework. We discuss several message-passing protocols for parsing and provide reasons for sacrificing completeness of the parse in favor of efficiency based on a preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st

Collaborative editing of knowledge resources for cross-lingual text mining

The need to smoothly deal with textual documents expressed in different languages is increasingly becoming a relevant issue in modern text mining environments. Recently the research on this field has been considerably fostered by the necessity for Web users to easily search and browse the growing amount of heterogeneous multilingual contents available on-line as well as by the related spread of the Semantic Web. A common approach to cross-lingual text mining relies on the exploitation of sets of properly structured multilingual knowledge resources. The involvement of huge communities of users spread over different locations represents a valuable aid to create, enrich, and refine these knowledge resources. Collaborative editing Web environments are usually exploited to this purpose. This thesis analyzes the features of several knowledge editing tools, both semantic wikis and ontology editors, and discusses the main challenges related to the design and development of this kind of tools. Subsequently, it presents the design, implementation, and evaluation of the Wikyoto Knowledge Editor, called also Wikyoto. Wikyoto is the collaborative editing Web environment that enables Web users lacking any knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project. To experiment real benefits from social editing of knowledge resources, it is important to provide common Web users with simplified and intuitive interfaces and interaction patterns. Users need to be motivated and properly driven so as to supply information useful for cross-lingual text mining. In addition, the management and coordination of their concurrent editing actions involve relevant technical issues. In the design of Wikyoto, all these requirements have been considered together with the structure and the set of knowledge resources exploited by KYOTO. Wikyoto aims at enabling common Web users to formalize cross-lingual knowledge by exploiting simplified language-driven interactions. At the same time, Wikyoto generates the set of complex knowledge structures needed by computers to mine information from textual contents. The learning curve of Wikyoto has been kept as shallow as possible by hiding the complexity of the knowledge structures to the users. This goal has been pursued by both enhancing the simplicity and interactivity of knowledge editing patterns and by using natural language interviews to carry out the most complex knowledge editing tasks. In this context, TMEKO, a methodology useful to support users to easily formalize cross-lingual information by natural language interviews has been defined. The collaborative creation of knowledge resources has been evaluated in Wikyoto

Cognitive flexibility predicts early reading skills

International audienceAn important aspect of learning to read is efficiency in accessing different kinds of linguistic information (orthographic, phonological, and semantic) about written words. The present study investigates whether, in addition to the integrity of such linguistic skills, early progress in reading may require a degree of cognitive flexibility in order to manage the coordination of this information effectively. Our study will look for evidence of a link between flexibility and both word reading and passage reading comprehension, and examine whether any such link involves domain-general or reading-specific flexibility. As the only previous support for a predictive relationship between flexibility and early reading comes from studies of reading comprehension in the opaque English orthography, another possibility is that this relationship may be largely orthography-dependent, only coming into play when mappings between representations are complex and polyvalent. To investigate these questions, 60 second-graders learning to read the more transparent French orthography were presented with two multiple classification tasks involving reading-specific cognitive flexibility (based on words) and non-specific flexibility (based on pictures). Reading skills were assessed by word reading, pseudo-word decoding, and passage reading comprehension measures. Flexibility was found to contribute significant unique variance to passage reading comprehension even in the less opaque French orthography. More interestingly, the data also show that flexibility is critical in accounting for one of the core components of reading comprehension, namely, the reading of words in isolation. Finally, the results constrain the debate over whether flexibility has to be reading-specific to be critically involved in reading