Previous work demonstrated that Web counts can be used to approximate bigram counts, suggesting that Web-based frequencies should be useful for a wide variety of Natural Language Processing (NLP) tasks. However, only a limited number of tasks have so far been tested using Web-scale data sets. The present article overcomes this limitation by systematically investigating the performance of Web-based models for several NLP tasks, covering both syntax and semantics, both generation and analysis, and a wider range of n-grams and parts of speech than have been previously explored. For the majority of our tasks, we find that simple, unsupervised models perform better when n-gram counts are obtained from the Web rather than from a large corpus. In some cases, performance can be improved further by using backoff or interpolation techniques that combine Web counts and corpus counts. However, unsupervised Web-based models generally fail to outperform supervised state-ofthe-art models trained on smaller corpora. We argue that Web-based models should therefore be used as a baseline for, rather than an alternative to, standard supervised models.

We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns. As corpora we use the British National Corpus (BNC), as well as the Web, which has not been previously used for this task. Our results show that (a) the knowledge encoded in WordNet is often insufficient, especially for anaphor– antecedent relations that exploit subjective or context-dependent knowledge; (b) for otheranaphora, the Web-based method outperforms the WordNet-based method; (c) for definite NP coreference, the Web-based method yields results comparable to those obtained using WordNet over the whole data set and outperforms the WordNet-based method on subsets of the data set; (d) in both case studies, the BNC-based method is worse than the other methods because of data sparseness. Thus, in our studies, the Web-based method alleviated the lexical knowledge gap often encountered in anaphora resolution and handled examples with context-dependent relations between anaphor and antecedent. Because it is inexpensive and needs no hand-modeling of lexical knowledge, it is a promising knowledge source to integrate into anaphora resolution systems. 1.

Abstract. We propose a method of acquiring attribute words for a wide range of objects from Japanese Web documents. The method is a simple unsupervised method that utilizes the statistics of words, lexico-syntactic patterns, and HTML tags. To evaluate the attribute words, we also establish criteria and a procedure based on question-answerability about the candidate word. 1

This paper addresses the problem of acquiring lexical semantic relationships, applied to the lexical entailment relation. Our main contribution is a novel conceptual integration between the two distinct acquisition paradigms for lexical relations – the patternbased and the distributional similarity approaches. The integrated method exploits mutual complementary information of the two approaches to obtain candidate relations and informative characterizing features. Then, a small size training set is used to construct a more accurate supervised classifier, showing significant increase in both recall and precision over the original approaches. 1

Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45 % more accurate on average, when acquired from query logs rather than Web documents. Categories and Subject Descriptors

Abstract. The Semantic Web needs ontologies as an integral component. Current methods for learning and enhancing ontologies, need to be further improved to overcome the knowledge acquisition bottleneck. The identification of concepts and relations with only minimal user interaction is still a challenging objective. Current approaches performed to extract semantics often use association rules or clustering upon regular flat text. In this paper we describe an approach on extracting semantics from Web Document collections which takes advantage of the semi structured content within XHTML (an XML dialect which can be obtained from traditional HTML documents) Web Documents. The XTREEM (Xhtml TREE Mining) method uses structural information, the mark-up in Web content, as indicators of term boundaries and for co-hyponymy relations. 1

Abstract. This paper describes an automatic dictionary construction method for Named Entity Recognition (NER) on specific domains such as restaurant guides. NER is the first step toward Information Extraction (IE), and we believe that such a dictionary construction method for NER is crucial for developing IE systems for a wide range of domains in the World Wide Web (WWW). One serious problem in NER on specific domains is that the performance of NER heavily depends on the amount of the training corpus, which requires much human labor to develop. We attempt to improve the performance of NER by using dictionaries automatically constructed from HTML documents instead of by preparing a large annotated corpus. Our dictionary construction method exploits the cooccurrence strength of two expressions in HTML itemizations calculated from average mutual information. Experimental results show that the constructed dictionaries improved the performance of the NER on a restaurant guide domain. Our method increased the F1-measure by 2.3 without any additional manual labor. 1

This paper presents a new method of developing a large-scale hyponymy relation database by combining Wikipedia and other Web documents. We attach new words to the hyponymy database extracted from Wikipedia by using distributional similarity calculated from documents on the Web. For a given target word, our algorithm first finds k similar words from the Wikipedia database. Then, the hypernyms of these k similar words are assigned scores by considering the distributional similarities and hierarchical distances in the Wikipedia database. Finally, new hyponymy relations are output according to the scores. In this paper, we tested two distributional similarities. One is based on raw verbnoun dependencies (which we call “RVD”), and the other is based on a large-scale clustering of verb-noun dependencies (called “CVD”). Our method achieved an attachment accuracy of 91.0 % for the top 10,000 relations, and an attachment accuracy of 74.5% for the top 100,000 relations when using CVD. This was a far better outcome compared to the other baseline approaches. Excluding the region that had very high scores, CVD was found to be more effective than RVD. We also confirmed that most relations extracted by our method cannot be extracted merely by applying the well-known lexicosyntactic patterns to Web documents. 1

We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus. Most earlier approaches to this problem rely on combining clusters of distributionally similar terms and conceptinstance pairs obtained with Hearst patterns. In contrast, our method relies on a novel approach for clustering terms found in HTML tables, and then assigning concept names to these clusters using Hearst patterns. The method can be efficiently applied to a large corpus, and experimental results on several datasets show that our method can accurately extract large numbers of concept-instance pairs. 1.