The French companion, consisting of familiar conversations on every topic which can be useful to the continental traveller;

[At head of title:]"Dean's stereotype ed."

A collection of colloquial phrases on every topic necessary to maintain conversation:

Mode of access: Internet.

A collection of colloquial phrases on every topic necessary to maintain conversation ; arranged under different heads ; with numerous remarks on the peculiar pronunciation and use of various words ; the whole so disposed as considerably to facilitate the acquisition of a correct pronunciation of the French /

Mode of access: Internet.

How 150 companies plan, prepare, and produce office manuals.

Loose-leaf.

Manuals of Emergency Legislation. Defence of the Realm Regulations

Mode of access: Internet.

Departmental & divisional manuals.

Mode of access: Internet.

Department store merchandise manuals.

Mode of access: Internet.

Theosophical manuals /

Some numbers also published in later editions, with later imprint dates.

Laboratory manuals of biological chemistry, with supplement,

Mode of access: Internet.

Multi-topic information filtering with a single user profile

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.