The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system’s performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.
In addition to these features, we have to consider that multiple citation markers may be present in a sentence. Thus, only certain parts of a sentence may be relevant to identifying the target of a particular citation marker. Qazvinian and Radev @cite_8 share an approach to find the fragment of a sentence that applies to a citation, especially in the case of sentences with multiple citation markers. The research of Abu-Jbara and Radev @cite_3 further argues that a fragment need not always be continguous.
@cite_3 @cite_8
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. In example (1), a sentence from a Wall Street Journal article taken from the Penn TreeBank corpus is further segmented into four EDUs, (1a), (1b), (1c) and (1d) (RST, 2002). Discourse segmentation, clearly, is not as easy as sentence boundary detection. The lack of consensus with regards to what constitutes an elementary discourse unit adds to the difficulty. Building a rule based discourse segmenter can be a tedious task since these rules would have to be based on the underlying grammar of the particular parser that is to be used. Therefore, we adopted a neural network model for automatically building a discourse segmenter from an underlying corpus of segmented text. We chose to use part-of-speech tags, syntactic information, discourse cues and punctuation. Our ultimate goal is to build a discourse parser that uses this discourse segmenter. The data that we used to train and test our discourse segmenter is the RST-DT (RST, 2002) corpus. The corpus contains 385 Wall Street Journal articles from the Penn Treebank. The training set consists of 347 articles for a total of 6132 sentences, whilst the test set contains 38 articles for a total of 991 sentences. The RST-DT corpus provides us with pairs of sentences and EDUs. For the syntactic structure of the sentences, we have used both the gold standard Penn Treebank data and syntactic parse trees generated by (Charniak, 2000). As regards the discourse cues, we used a list of 168 possible discourse markers. Textual entailment recognition is the task of deciding, when given two text fragments, whether the meaning of one text is entailed from the other text. This year, at our second participation in the RTE competition, we improve the system built for the RTE3 competition. The main idea of our system is to map every word from hypothesis to one or more words from the text. For that, we transform the hypothesis making use of extensive semantic knowledge from sources like DIRT, WordNet, VerbOcean, Wikipedia and Acronyms database. After the mapping process, we associate a local fitness value to every word from hypothesis, which is used to calculate a global fitness value for current fragments of text. The global fitness value is decreased in cases in which a word from hypothesis cannot be map to one word from the text or when we have different forms of negations for mapped verbs. In the end, using thresholds identified in the training step for global fitness values, we decide for every pair from test data if we have entailment or not. We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Bronte ¨, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long.