Topic links 2.9

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Internet Access in U.S. Public Schools and Classrooms– 1994-2001 - Anne Kleiner - Google knygosPrisijungtiPaslėpti laukaiKnygosMano bibliotekaPagalbaI

Internet Access in U.S. Public Schools and Classrooms: 1994-2001, September 2002 - Google knygosPrisijungtiPaslėpti laukaiKnygosMano bibliotekaPagalba

What happened to Topic Links 2.0 in the dark web? - Quora Something went wrong. Wait a moment and try again.Try again Please enable Javascript and ref

The papers in this volume focus on the impact of information structure on language acquisition, thereby taking different linguistic approaches into account. They start from an empirical point of view, and examine data from natural first and second language acquisition, which cover a wide range of varieties, from early learner language to native speaker production and from gesture to Creole prototypes. The central theme is the interplay between principles of information structure and linguistic structure and its impact on the functioning and development of the learner's system. The papers examine language-internal explanatory factors and in particular the communicative and structural forces that push and shape the acquisition process, and its outcome. On the theoretical level, the approach adopted appeals both to formal and communicative constraints on a learner s language in use. Two empirical domains provide a 'testing ground' for the respective weight of grammatical versus functional determinants in the acquisition process: (1) the expression of finiteness and scope relations at the utterance level and (2) the expression of anaphoric relations at the discourse level.

The "big data" era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media, news, and everyone's daily life. Examples of such collections include scientific publications, enterprise logs, news articles, social media, and general web pages. Valuable knowledge about multi-typed entities is often hidden in the unstructured or loosely structured, interconnected data. Mining latent structures around entities uncovers hidden knowledge such as implicit topics, phrases, entity roles and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive unstructured and interconnected data. We propose a text-rich information network model for modeling data in many different domains. This leads to a series of new principles and powerful methodologies for mining latent structures, including (1) latent topical hierarchy, (2) quality topical phrases, (3) entity roles in hierarchical topical communities, and (4) entity relations. This book also introduces applications enabled by the mined structures and points out some promising research directions.

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