While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally-similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. The tools will allow novel sources of information to be applied to long-standing natural language challenges. 1.

Web-scale data has been used in a diverse range of language research. Most of this research has used web counts for only short, fixed spans of context. We present a unified view of using web counts for lexical disambiguation. Unlike previous approaches, our supervised and unsupervised systems combine information from multiple and overlapping segments of context. On the tasks of preposition selection and context-sensitive spelling correction, the supervised system reduces disambiguation error by 20-24 % over the current state-of-the-art. 1

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text’s opinion towards its main subject matter. We show that SO-CAL’s performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability. 1.

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fundamental for linguistics and NLP. Unsupervised concept acquisition algorithms have been shown to produce good results, and are preferable over manual preparation of concept resources, which is labor intensive, error prone and somewhat arbitrary. Some existing concept mining methods utilize supervised language-specific modules such as POS taggers and computationally intensive parsers. In this paper we present an efficient fully unsupervised concept acquisition algorithm that uses syntactic information obtained from a fully unsupervised parser. Our algorithm incorporates the bracketings induced by the parser into the meta-patterns used by a symmetric patterns and graph-based concept discovery algorithm. We evaluate our algorithm on very large corpora in English and Russian, using both human judgments and WordNetbased evaluation. Using similar settings as the leading fully unsupervised previous work, we show a significant improvement in concept quality and in the extraction of multiword expressions. Our method is the first to use fully unsupervised parsing for unsupervised concept discovery, and requires no languagespecific tools or pattern/word seeds. 1

This paper investigates methods for using lexical patterns in a corpus to deduce the semantic relation that holds between two nouns in a noun-noun compound phrase such as “flu virus ” or “morning exercise”. Much of the previous work in this area has used automated queries to commercial web search engines. In our experiments we use the Google Web 1T corpus. This corpus contains every 2,3, 4 and 5 gram occurring more than 40 times in Google's index of the web, but has the advantage of being available to researchers directly rather than through a web interface. This paper evaluates the performance of the Web 1T corpus on the task compared to similar systems in the literature, and also investigates what kind of lexical patterns are most informative when trying to identify a semantic relation between two nouns. 1

Web count statistics gathered from search engines have been widely used as a resource in a variety of NLP tasks. For some tasks, however, the information they exploit is not fine-grained enough. We propose an inverted index over grammatical relations as a fast and reliable resource to access more general and also more detailed frequency information. To build the index, we use a dependency parser to parse a large corpus. We extract binary dependency relations, such as he-subj-say (he is the subject of say) as index terms and construct the index using publicly available open-source indexing software. The unit we index over is the sentence. The index can be used to extract grammatical relations and frequency counts for these relations. The framework also provides the possibility to search for partial dependencies (say, the frequency of he occurring in subject position), words, strings and a combination of these. One possible application is the disambiguation of syntactic structures. 1.

The ANC was motivated by developers of major linguistic resources such as FrameNet 1 and Nomlex, 2 who had been extracting usage examples from the 100 million-word British National Corpus (BNC), the largest corpus of English across several genres that was available at the time. These examples, which served as the basis for developing templates for the description of semantic arguments and the like, were often unusable or misrepresentative due to significant syntactic differences between British and American English. As a result, in 1998 a group of computational linguists proposed the creation of an American counterpart to the BNC, in order to provide examples of contemporary American English usage for computational linguistics research and resource development (Fillmore, Ide, Jurafsky, & Macleod, 1998). With that proposal, the ANC project was born. The ANC project was originally conceived as a near-identical twin to its British cousin: The ANC would include the same amount of data (100 million words), balanced over the same range of genres and including 10 % spoken transcripts just like the BNC. As for the BNC, funding for the ANC would be sought from publishers who needed American language data for the development of major dictionaries, thesauri, language learning textbooks, et cetera. However, beyond these similarities, the ANC was planned from the outset to differ from the BNC in a few significant ways. First, additional

This report describes a variety of work that uses web-scale N-gram data. This

In this paper, we present GLB, yet another open source and free system to create and exploit linguistic corpora gathered from the web. A simple, robust web crawl algorithm, a multi-dimensional information retrieval tool, and a crude parallelization mechanism are proposed, especially for researchers working in resource-limited environments.

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