Base_JSON/prefixP/json/P16/P16-1037.json

{
    "paper_id": "P16-1037",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T09:00:10.894357Z"
    },
    "title": "News Citation Recommendation with Implicit and Explicit Semantics",
    "authors": [
        {
            "first": "Hao",
            "middle": [],
            "last": "Peng",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Peking University",
                "location": {
                    "postCode": "100871",
                    "settlement": "Beijing",
                    "country": "China"
                }
            },
            "email": ""
        },
        {
            "first": "Jing",
            "middle": [],
            "last": "Liu",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Microsoft Research",
                "location": {
                    "postCode": "100080",
                    "settlement": "Beijing",
                    "country": "China"
                }
            },
            "email": ""
        },
        {
            "first": "Chin-Yew",
            "middle": [],
            "last": "Lin",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Microsoft Research",
                "location": {
                    "postCode": "100080",
                    "settlement": "Beijing",
                    "country": "China"
                }
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. Due to the sparsity issue of news citations and the engineering difficulty in obtaining information on authors, we focus on content similarity-based methods instead of collaborative filtering-based approaches. In this paper, we explore word embedding (i.e., implicit semantics) and grounded entities (i.e., explicit semantics) to address the variety and ambiguity issues of language. We formulate the problem as a reranking task and integrate different similarity measures under the learning to rank framework. We evaluate our approach on a real-world dataset. The experimental results show the efficacy of our method.",
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        "paper_id": "P16-1037",
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        "abstract": [
            {
                "text": "In this work, we focus on the problem of news citation recommendation. The task aims to recommend news citations for both authors and readers to create and search news references. Due to the sparsity issue of news citations and the engineering difficulty in obtaining information on authors, we focus on content similarity-based methods instead of collaborative filtering-based approaches. In this paper, we explore word embedding (i.e., implicit semantics) and grounded entities (i.e., explicit semantics) to address the variety and ambiguity issues of language. We formulate the problem as a reranking task and integrate different similarity measures under the learning to rank framework. We evaluate our approach on a real-world dataset. The experimental results show the efficacy of our method.",
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                "section": "Abstract",
                "sec_num": null
            }
        ],
        "body_text": [
            {
                "text": "When an author writes an online news article, s/he often cites previously published news reports to elaborate a mentioned event or support his/her point of view. For the convenience of the readers, the editor usually associates the words with hyperlinks. Through the links the readers can directly access the referenced articles to know more details about the events. If there is no reference for a mentioned event, the readers may search the related news reports for further reading. Hence, it is valuable to have automatic news citation recommendations for authors and readers to create or search news references.",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "In this paper, we focus on the problem of news citation recommendation. As shown in given a snippet of citing context (left), the task aims to retrieve a list of news articles (right) as references. This task differs from traditional recommendation tasks, e.g., citation recommendation for scientific papers, in that: (a) based on the statistics from our dataset, the number of references per news article is 4.56 on average, much less than the number of citations per academic paper (typically dozens); (b) the author-topic information is usually unavailable, since it is technically difficult to obtain author information from news articles. These differences make the collaborative filteringbased methods, which have been widely applied to paper citation recommendation, less available in our scenario. Therefore, in this paper we focus on content similarity-based methods to deal with the task of news citation recommendation.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Previous studies use string-based overlap (Xu et al., 2014) , machine translation measures (Madnani et al., 2012) , and dependency syntax (Wan et al., 2006; to model text similarity. More recent work focuses on neural network methods (Yin and Sch\u00fctze, 2015; Hu et al., 2014; dos Santos et al., 2015; Lei et al., 2016 ). There are two major challenges rendering these approaches not suitable for this task: (i) the variety and (ii) the ambiguity of language. By variety, we mean that the same meaning may be expressed with different phrases. Taking the first row in Table 1 for example, Vlaar in the citing context refers to Ron Vlaar, a Dutch football player, who is referred to as Dutch star and Netherlands international in the cited article. By ambiguity, we mean that the same expression may have different meanings in different contexts. In the second example in Table 1 , the mention tiger refers to tiger the mammal. By contrast, in \"Detroit Tigers links: The Tigers are in trouble\" for example, the word Tiger is the name of a team. In this paper, we explore both implicit and explicit semantics to ad-Citing Context Cited Article",
                "cite_spans": [
                    {
                        "start": 42,
                        "end": 59,
                        "text": "(Xu et al., 2014)",
                        "ref_id": "BIBREF41"
                    },
                    {
                        "start": 91,
                        "end": 113,
                        "text": "(Madnani et al., 2012)",
                        "ref_id": "BIBREF19"
                    },
                    {
                        "start": 138,
                        "end": 156,
                        "text": "(Wan et al., 2006;",
                        "ref_id": "BIBREF36"
                    },
                    {
                        "start": 234,
                        "end": 257,
                        "text": "(Yin and Sch\u00fctze, 2015;",
                        "ref_id": "BIBREF43"
                    },
                    {
                        "start": 258,
                        "end": 274,
                        "text": "Hu et al., 2014;",
                        "ref_id": "BIBREF11"
                    },
                    {
                        "start": 275,
                        "end": 299,
                        "text": "dos Santos et al., 2015;",
                        "ref_id": "BIBREF6"
                    },
                    {
                        "start": 300,
                        "end": 316,
                        "text": "Lei et al., 2016",
                        "ref_id": "BIBREF17"
                    }
                ],
                "ref_spans": [
                    {
                        "start": 565,
                        "end": 572,
                        "text": "Table 1",
                        "ref_id": "TABREF0"
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                        "start": 868,
                        "end": 875,
                        "text": "Table 1",
                        "ref_id": "TABREF0"
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                ],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Man United and Arsenal on red alert as top Dutch star officially joins free agent list Manchester United and Arsenal have both been interested in Vlaar in the past, suggesting Southampton will have to fight hard to land him.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "The Netherlands international has joined the free agent list today and is no longer contractually obliged to remain at Villa Park.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "Bangladesh 's abundant tiger population has collapsed to just 100",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "Conservationists want the Bangladeshi government to step up and help save the tigers through greater administration and enforcement of anti-poaching laws, as Bangladesh does not legally protect tigers to the extent that other governments do, according to Inhabitat.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "In Bangladesh, a new census shows that tiger populations in the Sundarbans mangrove forest are more endangered than ever. The study, which used hidden cameras to track and record tigers, provides a more accurate update than previous surveys that used other methods. The year-long census, which ended this April, revealed only around 100 of the big cats remain in what was once home to the largest population of tigers on earth. Table 1 : Two pair of news snippets. For readability concerns, we keep only the sentence associated with an anchor link in the citing part, and the title and lead paragraph of the cited part.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 428,
                        "end": 435,
                        "text": "Table 1",
                        "ref_id": "TABREF0"
                    }
                ],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "dress the above issues. Specifically, the implicit semantics can be obtained from the word embedding trained on large scale corpus, and the explicit semantics through linking entity mentions to the grounded entities in a knowledge base.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "In this paper, we explore using both word embedding and grounded knowledge to model the relatedness between citing context and articles. We formulate the problem as a re-ranking task. We use learning to rank to integrate different similarity measures and evaluate the models on a real world dataset constructed from Bing News 1 . We further give quantitative analysis of the effects of word embedding and grounded entities in the task.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "In summary, the main contributions of this paper are three-fold:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "\u2022 We propose the task of news citation recommendation and construct a real-world dataset for this task. \u2022 We utilize both word embedding based similarity measures and knowledge-based methods to tackle the problem. We formulate the problem as a re-ranking task and leverage learning to rank algorithm to integrate different similarity measures. \u2022 We conduct extensive experiments on a large dataset. The results show the effectiveness of word embedding and grounded entities. We further quantitatively analyze how the implicit semantics from word embedding and explicit semantics from grounded knowledge benefit the task of interest.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "1 https://www.bing.com/news",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "\u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022",
                "sec_num": null
            },
            {
                "text": "In this section, we introduce the news citation recommendation problem and formulate it as a reranking task. We first introduce definitions that will be used through the rest of the paper:",
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                "eq_spans": [],
                "section": "Problem Formulation",
                "sec_num": "2"
            },
            {
                "text": "Citing Context. Citing context is a sentence which contains an anchor text associated with a hyperlink. As shown in Table 1 , the underlined words are associated with a hyperlink pointing to another news article, and the sentence (left) which contains the anchor is the citing context.",
                "cite_spans": [],
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                    {
                        "start": 116,
                        "end": 123,
                        "text": "Table 1",
                        "ref_id": "TABREF0"
                    }
                ],
                "eq_spans": [],
                "section": "Problem Formulation",
                "sec_num": "2"
            },
            {
                "text": "Cited Article. Given a piece of citing context, the article that the hyperlink links to is defined as its cited article. It is expected that a news article is well-structured, and its headline together with its lead paragraph gives a good brief description of the whole story (Kianmehr et al., 2009) . In this paper, a news article can either be represented by its title and lead paragraph or by the passage as a whole. We conduct experiments under both of the two different settings.",
                "cite_spans": [
                    {
                        "start": 276,
                        "end": 299,
                        "text": "(Kianmehr et al., 2009)",
                        "ref_id": "BIBREF14"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Problem Formulation",
                "sec_num": "2"
            },
            {
                "text": "Candidate Article Set. Considering efficiency, we follow the procedure adopted by many recommendation systems (Lei et al., 2016; Tan et al., 2015) and formulate the problem as a re-ranking task. In another word, given a citing context, we first use efficient retrieval methods with high recall to generate a list of articles as the candidate article set, and then run the system to get a re-ranked list.",
                "cite_spans": [
                    {
                        "start": 110,
                        "end": 128,
                        "text": "(Lei et al., 2016;",
                        "ref_id": "BIBREF17"
                    },
                    {
                        "start": 129,
                        "end": 146,
                        "text": "Tan et al., 2015)",
                        "ref_id": "BIBREF31"
                    }
                ],
                "ref_spans": [],
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                "section": "Problem Formulation",
                "sec_num": "2"
            },
            {
                "text": "News Citation Recommendation. Given a citing context, the task aims to construct an ordered list of news articles, top of which are most relevant to the context, and can serve as the cited articles.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Problem Formulation",
                "sec_num": "2"
            },
            {
                "text": "In this section, we first explain the similarity measures based on word embedding (implicit semantics) and grounded knowledge (explicit semantics) to deal with variety and ambiguity problems. Then we briefly introduce the baselines and the learning to rank framework.",
                "cite_spans": [],
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                "section": "Method",
                "sec_num": "3"
            },
            {
                "text": "The distributed word representation by word2vec factors word distance and captures semantic similarities through vector arithmetic (Mikolov et al., 2013) . In this work, we train a skip-gram model to bridge the vocabulary gap between context-article pairs. Previous work represents the documents with averaged vectors of words (Tang et al., 2014; Tan et al., 2015) . However, this may lead to the loss of detailed information of the documents. In this paper, we adopt a different approach, explained below.",
                "cite_spans": [
                    {
                        "start": 131,
                        "end": 153,
                        "text": "(Mikolov et al., 2013)",
                        "ref_id": "BIBREF22"
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                        "start": 327,
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                        "text": "(Tang et al., 2014;",
                        "ref_id": "BIBREF33"
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                        "start": 347,
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                        "text": "Tan et al., 2015)",
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "Word Mover's Distance (WMD). Kusner et al. (2015) combine distributed word representations with the earth mover's distance (EMD) (Rubner et al., 1998; Wan, 2007) to measure the distance between documents. They use the Euclidean distance between words' low dimensional representations as building blocks, and optimize a special case of the EMD to obtain the cumulative distance. More formally, let",
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                    {
                        "start": 29,
                        "end": 49,
                        "text": "Kusner et al. (2015)",
                        "ref_id": "BIBREF15"
                    },
                    {
                        "start": 129,
                        "end": 150,
                        "text": "(Rubner et al., 1998;",
                        "ref_id": "BIBREF27"
                    },
                    {
                        "start": 151,
                        "end": 161,
                        "text": "Wan, 2007)",
                        "ref_id": "BIBREF37"
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "X = {(x 1 , w x 1 ), (x 2 , w x 2 ), \u2022 \u2022 \u2022 , (x m , w",
                "cite_spans": [],
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
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            {
                "text": "xm )} be the normalized bag-of-words representation for a citing context after removing stop-words, where word x i appears w x i times (then normalized by the total count of words in X), i = 1, 2, \u2022 \u2022 \u2022 , m. Similarly, we have the representation for a candidate article,",
                "cite_spans": [],
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "Y ={(y 1 , w y 1 ), (y 2 , w y 2 ), \u2022 \u2022 \u2022 , (y n , w yn )}.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "The WMD calculates the minimum cumulative cost by solving the linear programming problem below:",
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "min T m i=1 n j=1 T ij c ij s.t. n j=1 T ij = w x i , i = 1, 2, \u2022 \u2022 \u2022 , m, m i=1 T ij = w y j , j = 1, 2, \u2022 \u2022 \u2022 , n,",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "where T \u2208 R m\u00d7n is the transportation flow matrix, and c ij indicates the distance between x i and y j . Here",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "c ij = vector(x i ) \u2212 vector(y j )",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
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                "text": ", where function vector(w) returns the word vector of w. Then the distance is normalized by the total flow:",
                "cite_spans": [],
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "W M D(X, Y ) = m i=1 n j=1 T ij c ij m i=1 n j=1 T ij",
                "cite_spans": [],
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                "section": "Implicit Semantics for Variety",
                "sec_num": "3.1"
            },
            {
                "text": "News articles tend to be well written, and contain many named entity mentions. Making use of this property, we deal with the ambiguity problem by using grounded entities (explicit semantics). Given a context-article pair, we first recognize all named entity mentions on both sides and link them to knowledge bases (e.g., Wikipedia and Freebase), then use the following measures to model the similarity.",
                "cite_spans": [],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "\u2022 Entity Overlap. Given a context-article pair, we consider two metrics, namely, precision and recall, to measure their entity overlap.",
                "cite_spans": [],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "The precision is defined as:",
                "cite_spans": [],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "precision = entity-overlap(citing, cited) entity-count(citing)",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "and recall as:",
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "recall = entity-overlap(citing, cited) entity-count(cited)",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
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                "text": "\u2022 Embedding Based Matching. We build two separate information networks for Wikipedia entities using (a) the anchor links on Wikipedia pages and (b) the Freebase entity graph (Bollacker et al., 2008) . Then we apply Large-scale Information Network Embedding (LINE) (Tang et al., 2015 ) system 2 to the networks to embed the entities into low-dimensional spaces. We then measure the similarity by the minimized cosine distance between entities' on the citing and the cited side:",
                "cite_spans": [
                    {
                        "start": 174,
                        "end": 198,
                        "text": "(Bollacker et al., 2008)",
                        "ref_id": "BIBREF3"
                    },
                    {
                        "start": 264,
                        "end": 282,
                        "text": "(Tang et al., 2015",
                        "ref_id": "BIBREF34"
                    }
                ],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "minDISciting = 1 |citing| |citing| i=1 min y j \u2208Y (1\u2212cos(xi, yj)),",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "and vice versa:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "minDIS cited = 1 |citied| |cited| j=1 min x i \u2208X (1 \u2212 cos(xi, yj)),",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "where X refers to the citing context, and x i \u2208 X are the grounded entities in the citing part. Similar with Y and y j .",
                "cite_spans": [],
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                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
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                "text": "\u2022 Wikipedia Evidence. Given a contextarticle pair, we refer to world knowledge for supporting evidence. In particular, we first apply an entity linking system to detect the entity mentions on both sides and ground them into Wikipedia entries, each of which has its own description page. Second, we collect the descriptions for entities from the candidate article and extract as evidence those sentences containing entities from citing context. We refer to this evidence as cited evidence. For instance, the article in Table 8 contains grounded entity Scottish National Party. And in the description for it, there is a sentence containing the entity Scotland from the citing context: \"The Scottish National Party (SNP) is a Scottish nationalist and social-democratic political party in Scotland.\" Thus we extract this sentence as cited evidence supporting this pair. We count the overlapping nouns between the citing context and the cited evidence to calculate precision and recall, precision = noun-overlap(context, cited evidence) noun-count(context) recall = noun-overlap(article, citing evidence) noun-count(article)",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 518,
                        "end": 525,
                        "text": "Table 8",
                        "ref_id": "TABREF9"
                    }
                ],
                "eq_spans": [],
                "section": "Explicit Semantics for Ambiguity",
                "sec_num": "3.2"
            },
            {
                "text": "We design several baseline features for the two groups of features mentioned above:",
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                "eq_spans": [],
                "section": "Baselines",
                "sec_num": "3.3"
            },
            {
                "text": "\u2022 TF-IDF Distance. We use TF-IDF distance as a basic measure. The similarity is calculated with cosine distance based on TF-IDF vector representations for the text. \u2022 Ungrounded Mentions. Note that entity overlap features also adapt to ungrounded mentions. The embedding-based matching features for ungrounded mentions are similar to those for grounded entities. The only difference is that here each mention is represented by the averaged vectors of all the words it contains. Wikipedia evidence is not feasible for ungrounded mentions. Table 2 summarizes all the features we use. A cited article can either be represented by its head-line+lead paragraph or as a whole. Therefore, we extract features under two different settings: (a) headlines and lead paragraphs only; (b) the full articles. Most of the features are extracted under both of the settings. However, feature 2 is much too computation-intensive and feature 7 needs POS-tagging as the preprocessing. Thus these two are only extracted under setting (a).",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 538,
                        "end": 545,
                        "text": "Table 2",
                        "ref_id": "TABREF2"
                    }
                ],
                "eq_spans": [],
                "section": "Baselines",
                "sec_num": "3.3"
            },
            {
                "text": "Many different learning to rank algorithms have been proposed to deal with the ranking problem, including pointwise, pairwise, and listwise approaches (Xia et al., 2008) . Listwise methods receive ranked lists as training samples, and directly optimize the metric of interest by minimizing the respective cost. And it has been reported that the listwise method usually achieves better performance compared to others (Qin et al., 2008; Cao et al., 2007) . In this work, we use the linear model and apply coordinate ascent for parameter optimization.",
                "cite_spans": [
                    {
                        "start": 151,
                        "end": 169,
                        "text": "(Xia et al., 2008)",
                        "ref_id": "BIBREF40"
                    },
                    {
                        "start": 416,
                        "end": 434,
                        "text": "(Qin et al., 2008;",
                        "ref_id": "BIBREF23"
                    },
                    {
                        "start": 435,
                        "end": 452,
                        "text": "Cao et al., 2007)",
                        "ref_id": "BIBREF4"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Learning to Rank Framework",
                "sec_num": "3.4"
            },
            {
                "text": "We collect one month's news articles from Bing News. The citing context set consists of all the sentences associated with anchor link(s). For each piece of citing context, its cited article is extracted through its hyperlink. If there are multiple links associated with the context, only the first one is considered. We pair each citing context and its cited article as a ground truth sample. We further label as ground truths those articles sharing the same title as the cited article. This is rather reasonable since a single passage may have multiple reprints by difference sources. On average, there are 2.20 ground truth cited articles for each citing context in the dataset.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data Collection",
                "sec_num": "4.1"
            },
            {
                "text": "In order to focus only on news events, we filter out those pairs whose hyperlinks are associated with three words or less (usually names for persons or places, and lead to definition pages). We also discard those samples whose citing contexts contain or are exactly the same as the titles of the cited articles. For example, \"READ MORE: The stories you need to read, in one handy email\" links to an article titled \"The stories you need to read, in one handy email\".",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Data Collection",
                "sec_num": "4.1"
            },
            {
                "text": "The dataset is preprocessed with Stanford CoreNLP toolkit (Manning et al., 2014) , including sentence splitting, tokenizing for whole passages, and POS-tagging for titles and lead paragraphs. We use the JERL system by Luo et al. (2015) for entity detection and grounding. It recognizes entity mentions and links them to Wikipedia entries. We use each mention's text span as an ungrounded mention, and its corresponding Wikipedia ID as a grounded entity. For instance, in Table 8 , the detected text span Westminster is an ungrounded mention, and it's grounded to the entry Parliament of the United Kindom.",
                "cite_spans": [
                    {
                        "start": 58,
                        "end": 80,
                        "text": "(Manning et al., 2014)",
                        "ref_id": "BIBREF20"
                    },
                    {
                        "start": 218,
                        "end": 235,
                        "text": "Luo et al. (2015)",
                        "ref_id": "BIBREF18"
                    }
                ],
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                    {
                        "start": 471,
                        "end": 478,
                        "text": "Table 8",
                        "ref_id": "TABREF9"
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                ],
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                "section": "Data Collection",
                "sec_num": "4.1"
            },
            {
                "text": "Given a citing context, we construct its candidate article set with the top 200 articles retrieved by TF-IDF distance. In the experiments, approximately 92.61% of the ground truth cited articles appear in the candidate sets. We discard those that do not. We further randomly split the remaining 33318 pairs into training/validation/test sets with the proportion of 3:1:1. For each training pair, we randomly sample 5 articles from its candidate article set (excluding ground truth) and pair them with the citing context as negative samples. According to Tan et al. (2015) , the number of negative samples does not significantly affect the linear learning to rank model's performance. During validation and testing, all of the 200 candidates are taken into account.",
                "cite_spans": [
                    {
                        "start": 554,
                        "end": 571,
                        "text": "Tan et al. (2015)",
                        "ref_id": "BIBREF31"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Selecting Candidates",
                "sec_num": "4.2"
            },
            {
                "text": "In the experiments, we set the TF-IDF as the baseline, and incrementally add different groups of features to the system.",
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                "ref_spans": [],
                "eq_spans": [],
                "section": "Experimental Setup",
                "sec_num": "4.3"
            },
            {
                "text": "The word embedding is pretrained with skipgram model (Mikolov et al., 2013) on Wikipedia corpus and then fine-tuned using the method proposed in Wieting et al. (2015) on PPDB (Ganitkevitch et al., 2013) . The embedding fine-tuned with paraphrase pairs can better capture the semantic relatedness of different phrase. In the experiments, we observe a 1% \u2212 2% improvement by the finetuned word representations compared to vanilla skip-gram vectors.",
                "cite_spans": [
                    {
                        "start": 53,
                        "end": 75,
                        "text": "(Mikolov et al., 2013)",
                        "ref_id": "BIBREF22"
                    },
                    {
                        "start": 145,
                        "end": 166,
                        "text": "Wieting et al. (2015)",
                        "ref_id": "BIBREF39"
                    },
                    {
                        "start": 175,
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                        "text": "(Ganitkevitch et al., 2013)",
                        "ref_id": "BIBREF9"
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                ],
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                "eq_spans": [],
                "section": "Experimental Setup",
                "sec_num": "4.3"
            },
            {
                "text": "We use the linear model in RankLib 3 for the learning to rank implementation. Coordinate ascent is used for parameter optimization. The model is trained to directly optimize the evaluation metrics, Precision@1, Precision@5, NDCG@5 and MAP, respectively.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Experimental Setup",
                "sec_num": "4.3"
            },
            {
                "text": "For NDCG@5 measure, we set a binary relevance score, i.e., the scores equal to 1 for ground truths, 0 for negative samples. Table 3 gives the performance of the baselines and the systems using different groups of features on test and validation sets. The results show that WMD brings a consistent improvement over its TF-IDF baseline, and so do grounded entities compared to ungrounded mentions.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 124,
                        "end": 131,
                        "text": "Table 3",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Experimental Setup",
                "sec_num": "4.3"
            },
            {
                "text": "Individually added to the TF-IDF baseline, WMD has the largest performance boost, followed by grounded entity features. Besides, the additional information from grounded entity knowledge helps the model outperform the ungrounded mentions, with a consistent margin of 1.0%-2.0% NDCG@5.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Experimental Results",
                "sec_num": "4.4"
            },
            {
                "text": "We further compare the performance of the models when using features from headlines+lead paragraphs only and those from full passages. As shown in Table 3 , the former brings much better performance on each metric compared to the latter.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 147,
                        "end": 154,
                        "text": "Table 3",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Experimental Results",
                "sec_num": "4.4"
            },
            {
                "text": "It's worth noting that there are ground truths mis-labeled as irrelevant in the dataset. A primary reason is that news sites sometimes individually publish different reports on a certain event. And the articles don't necessarily share the same title. To see how this affects the model, we randomly build a subset S of the test set and manually label the selected samples, which givesS 4 . Table  4 compares the model's performance on S andS under Headline+Lead paragraph setting. There is a consistent improvement of NDCG@5 score o\u00f1 S compared to that on S. Besides that, on manually labeled data, the model's performance across different feature settings is almost in accord with that on the full test set. These results show that there are indeed mis-labeled ground truths in the dataset, but they have little influence when comparing different groups of features.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 389,
                        "end": 397,
                        "text": "Table  4",
                        "ref_id": "TABREF5"
                    }
                ],
                "eq_spans": [],
                "section": "Experimental Results",
                "sec_num": "4.4"
            },
            {
                "text": "In this section, we give detailed win-loss analysis for the models trained with NDCG@5 metric under headlines+lead paragraphs setting. Specifically, given two systems with different feature configurations, we compare their performance on each test sample. The results are shown as a heatmap in Figure 1 . X and Y axises indicate the identifiers for each feature group, following those in Table 3 . For example, the data point at (5, 1) indicates that the inclusion of WMD brings better ranking scores to TF-IDF on 18.4% of the test samples; and as a trade off, it lowers the scores on 11.4% of the samples. We also observe that grounded entities brings gain to 15.9% of the samples, and loss for 9.6% of them. On average, two different groups of features disagree on 26.4% of the test samples. We further give several mis-predictions by the model using certain groups of features, and illustrate how they are corrected by the inclusion of others (or the other way round). By misprediction, we mean that no ground truth cited article appears in the top 5 predictions of the returned list. Table 5 shows a mis-prediction by TF-IDF, but corrected after including WMD.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 294,
                        "end": 302,
                        "text": "Figure 1",
                        "ref_id": null
                    },
                    {
                        "start": 388,
                        "end": 395,
                        "text": "Table 3",
                        "ref_id": "TABREF4"
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                    {
                        "start": 1088,
                        "end": 1095,
                        "text": "Table 5",
                        "ref_id": "TABREF6"
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                ],
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                "section": "Analysis",
                "sec_num": "5"
            },
            {
                "text": "TF-IDF distance favors the high-score match-ing keywords approval and rating between the citing context and mis-predicted article. On the other hand, distributed word representations factor the distances between word pairs, which helps to capture their semantic closeness, e.g., (Argentines, Argentina, Cosince distance: 0.210), (poll, election, 0.020), and (increasingly, growing, 0.286). WMD helps to bridge the vocabulary gap between the citing context and the cited article. On the other hand, though not often, the use of distributed representation can also create mistakes. Table 6 gives an example where the inclusion of the WMD feature changes a correct prediction by TF-IDF into a mistake. By analyzing the WMD's transportation flow matrix T, we find that the used word embedding relates MP to minister, and publicly to government. More curiously, persons' names are very similar in its semantic space: (Davies, Stephen, 0.602), and (Davies, Harper, 0.635). A possible reason could be that both of the two names are very common, and thus the cooccurrence-based representation learning method is not able to distinguish them. This also justifies our use of grounded entities as additional information: from the Wikipedia description for entity Stephen Harper, the system might be able to find out that he actually serves in Canadian government, not in the UK's nor in the Welsh.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 580,
                        "end": 587,
                        "text": "Table 6",
                        "ref_id": "TABREF7"
                    }
                ],
                "eq_spans": [],
                "section": "Dealing with Variety",
                "sec_num": "5.1"
            },
            {
                "text": "Entity grounding helps by resolving the ambiguity e.g., alias, abbreviation, of the entity mentions.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Dealing with Ambiguity",
                "sec_num": "5.2"
            },
            {
                "text": "As shown in Table 7 , tiger refers to the mammal in the ground truth pair. However, the same word refers to Detroit Tigers the team in the mispredicted article. This ambiguity is resolved when the mention is grounded to its Wikipedia entry. In another example shown in Table 8 , ungrounded mention SNP, though detected, contributes little to supporting the ground truth pair. However, when it's grounded to the entry Scottish National Party, the system leverages world knowledge and relates it to the mention of Scotland in the citing context.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 12,
                        "end": 19,
                        "text": "Table 7",
                        "ref_id": "TABREF8"
                    },
                    {
                        "start": 269,
                        "end": 276,
                        "text": "Table 8",
                        "ref_id": "TABREF9"
                    }
                ],
                "eq_spans": [],
                "section": "Dealing with Ambiguity",
                "sec_num": "5.2"
            },
            {
                "text": "The inclusion of grounded entity information may also lead to mistakes, many of which are due to the limited performance of the entity recognition and disambiguation system. We'd like to discuss another kind of error here, shown in Table  9 . In the citing context, The Daily Telegraph is a newspaper published in the UK. It has little to do with the involved event except for reporting it. However, the system favors a farmers' story which Figure 1 : Heatmap for win-loss analysis results. Point (x, y) indicates how much feature x wins (loses if negative) against y.The X and Y axises indicate the identifiers for each feature group, following those in Table 3. actually happened in the UK. We find that this contributes a lot to the system's errors when including grounded entities. We leave it to future work to figure out how to deal with this issue.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 232,
                        "end": 240,
                        "text": "Table  9",
                        "ref_id": "TABREF10"
                    },
                    {
                        "start": 441,
                        "end": 449,
                        "text": "Figure 1",
                        "ref_id": null
                    },
                    {
                        "start": 655,
                        "end": 663,
                        "text": "Table 3.",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Dealing with Ambiguity",
                "sec_num": "5.2"
            },
            {
                "text": "This section reviews three lines of related work: (i) document recommendation, (ii) pharaphrase identification, (iii) question retrieval.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Related Work",
                "sec_num": "6"
            },
            {
                "text": "Existing literature mainly focuses on contentbased methods and collaborative filtering (Adomavicius and Tuzhilin, 2005) . There are studies trying to recommend documents based on citation contexts, either through identifying the motivations of the citations (Aya et al., 2005) , or through the topical similarity (Ritchie, 2008; Ramachandran and Amir, 2007) . On the other hand, Mcnee et al. (2002) leverage multiple information sources from authorship, paper-citation relations, and cocitations to recommend research papers.",
                "cite_spans": [
                    {
                        "start": 87,
                        "end": 119,
                        "text": "(Adomavicius and Tuzhilin, 2005)",
                        "ref_id": "BIBREF0"
                    },
                    {
                        "start": 258,
                        "end": 276,
                        "text": "(Aya et al., 2005)",
                        "ref_id": "BIBREF1"
                    },
                    {
                        "start": 313,
                        "end": 328,
                        "text": "(Ritchie, 2008;",
                        "ref_id": "BIBREF26"
                    },
                    {
                        "start": 329,
                        "end": 357,
                        "text": "Ramachandran and Amir, 2007)",
                        "ref_id": null
                    },
                    {
                        "start": 379,
                        "end": 398,
                        "text": "Mcnee et al. (2002)",
                        "ref_id": "BIBREF21"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Document Recommendation",
                "sec_num": "6.1"
            },
            {
                "text": "Combining the context-based approaches and collaborative filtering, Torres et al. (2004) and Strohman et al. (2007) report better performance. Tang and Zhang (2009) use the Restricted Boltzmann Machine to model citations for placeholders, and Tan et al. (2015) integrate multiple features to recommend quotes for writings.",
                "cite_spans": [
                    {
                        "start": 68,
                        "end": 88,
                        "text": "Torres et al. (2004)",
                        "ref_id": "BIBREF35"
                    },
                    {
                        "start": 93,
                        "end": 115,
                        "text": "Strohman et al. (2007)",
                        "ref_id": "BIBREF30"
                    },
                    {
                        "start": 143,
                        "end": 164,
                        "text": "Tang and Zhang (2009)",
                        "ref_id": "BIBREF32"
                    },
                    {
                        "start": 243,
                        "end": 260,
                        "text": "Tan et al. (2015)",
                        "ref_id": "BIBREF31"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Document Recommendation",
                "sec_num": "6.1"
            },
            {
                "text": "In the news domain, context-based approaches are presumably favorable due to the fact that the articles are relatively content-rich and citationsparse. Previous studies manage to utilize information retrieval techniques to recommend news articles given a seed article (Yang et al., 2009; Bogers and van den Bosch, 2007) .",
                "cite_spans": [
                    {
                        "start": 268,
                        "end": 287,
                        "text": "(Yang et al., 2009;",
                        "ref_id": "BIBREF42"
                    },
                    {
                        "start": 288,
                        "end": 319,
                        "text": "Bogers and van den Bosch, 2007)",
                        "ref_id": "BIBREF2"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Document Recommendation",
                "sec_num": "6.1"
            },
            {
                "text": "citing -An earlier poll showed Argentines are also increasingly happy with her performance as President, putting her approval rating at almost 43%, up from 31% in September.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Sides -Samples",
                "sec_num": null
            },
            {
                "text": "Kirchner's Growing Popularity Could Skew Argentine Election As Argentina gears up for a presidential election in October, the approval ratings of the current president, Cristina Kirchner, are improving and her rising reputation could affect the results of the election to replace her.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Ground Truth",
                "sec_num": null
            },
            {
                "text": "Top-1 Prediction Bill Shorten's Approval Rating Falls in Wake of Royal Commission The opposition leader gained approval from only 27% of the voters surveyed, while 52% disapproved. have much more time to waste if they want to stay in contention. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Ground Truth",
                "sec_num": null
            },
            {
                "text": "Several hand-crafted features have proven helpful in modeling sentence/phrase similarity, e.g., string-based overlap (Xu et al., 2014) , machine translation measures (Madnani et al., 2012) , and dependency syntax (Wan et al., 2006; . Using the combination and discriminative re-weighting of the mentioned features, Ji and Eisenstein (2013) manage to obtain more competitive results. More recent work has switched the focus onto neural methods. Socher et al. (2011) recursively encode the representations of sentences by the compositions of words. Convolutional neural nets (LeCun et al., 1998; Collobert and Weston, 2008) are also exploited in the tasks of paraphrase identification and sentence matching (Yin and Sch\u00fctze, 2015; Hu et al., 2014) .",
                "cite_spans": [
                    {
                        "start": 117,
                        "end": 134,
                        "text": "(Xu et al., 2014)",
                        "ref_id": "BIBREF41"
                    },
                    {
                        "start": 166,
                        "end": 188,
                        "text": "(Madnani et al., 2012)",
                        "ref_id": "BIBREF19"
                    },
                    {
                        "start": 213,
                        "end": 231,
                        "text": "(Wan et al., 2006;",
                        "ref_id": "BIBREF36"
                    },
                    {
                        "start": 444,
                        "end": 464,
                        "text": "Socher et al. (2011)",
                        "ref_id": "BIBREF29"
                    },
                    {
                        "start": 573,
                        "end": 593,
                        "text": "(LeCun et al., 1998;",
                        "ref_id": "BIBREF16"
                    },
                    {
                        "start": 594,
                        "end": 621,
                        "text": "Collobert and Weston, 2008)",
                        "ref_id": "BIBREF5"
                    },
                    {
                        "start": 705,
                        "end": 728,
                        "text": "(Yin and Sch\u00fctze, 2015;",
                        "ref_id": "BIBREF43"
                    },
                    {
                        "start": 729,
                        "end": 745,
                        "text": "Hu et al., 2014)",
                        "ref_id": "BIBREF11"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Paraphrase Identification",
                "sec_num": "6.2"
            },
            {
                "text": "Story link detection (SLD) is a similar task which aims to classify whether two news stories discuss the same event. Farahat et al. (2003) leverage part of speech tagging technique as well as task-specific similarity measures to boost the system's performance. Shah et al. (2006) show that entity based document representation is a better choice compared to word-based representations in SLD. In our scenario, the query is typically a piece of context sentence instead of an entire article. Therefore, we find that document level methods yield sub-optimal performance when used to model the similarity of citing context and the articles. Besides, due to the fact that there might be multiple reports for a single event, we consider it reasonable to formulate our problem into a ranking task instead of classification.",
                "cite_spans": [
                    {
                        "start": 117,
                        "end": 138,
                        "text": "Farahat et al. (2003)",
                        "ref_id": "BIBREF8"
                    },
                    {
                        "start": 261,
                        "end": 279,
                        "text": "Shah et al. (2006)",
                        "ref_id": "BIBREF28"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Paraphrase Identification",
                "sec_num": "6.2"
            },
            {
                "text": "The key problem in question retrieval lies in modeling questions' similarity. Machine translation techniques (Jeon et al., 2005) and topic models (Duan et al., 2008) have been utilized by previous works. An alternative is representation learning. Zhou et al. (2015) use category-based meta-data to learn word embeddings. dos Santos et al. 2015and Lei et al. (2016) obtain superior performance over hand-crafted features with CNN.",
                "cite_spans": [
                    {
                        "start": 109,
                        "end": 128,
                        "text": "(Jeon et al., 2005)",
                        "ref_id": "BIBREF12"
                    },
                    {
                        "start": 146,
                        "end": 165,
                        "text": "(Duan et al., 2008)",
                        "ref_id": "BIBREF7"
                    },
                    {
                        "start": 247,
                        "end": 265,
                        "text": "Zhou et al. (2015)",
                        "ref_id": "BIBREF44"
                    },
                    {
                        "start": 347,
                        "end": 364,
                        "text": "Lei et al. (2016)",
                        "ref_id": "BIBREF17"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Question Retrieval",
                "sec_num": "6.3"
            },
            {
                "text": "News articles are more well-written than most documents in QA communities, which results in the feasibility of high-quality entity detection and grounding.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Question Retrieval",
                "sec_num": "6.3"
            },
            {
                "text": "In this paper, we propose a novel problem of news citation recommendation, which aims to recommend news citations for references based on a citing context. We develop a re-ranking system leveraging implicit and explicit semantics for content similarity. We construct a real-world dataset. The experimental results show the efficacy of our approach.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discussions",
                "sec_num": "7"
            },
            {
                "text": "8 Acknowledgments",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discussions",
                "sec_num": "7"
            },
            {
                "text": "https://github.com/tangjianpku/LINE",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://sourceforge.net/p/lemur/wiki/ RankLib/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "Manually labeling all of the dev and test samples would be too time consuming, and we leave it to future work.",
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                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
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                "text": "",
                "num": null,
                "content": "<table/>"
            },
            "TABREF2": {
                "type_str": "table",
                "html": null,
                "text": "A list of all features used in the experiments. The third column indicates whether the corresponding feature is extracted from the full articles. If not, it's extracted only from the headlines and lead paragraphs.",
                "num": null,
                "content": "<table/>"
            },
            "TABREF4": {
                "type_str": "table",
                "html": null,
                "text": "Experimental results in percentage on the dataset collected from Bing News.",
                "num": null,
                "content": "<table><tr><td>id</td><td>Features</td><td>NDCG@5 on S</td><td>NDCG@5 onS</td></tr><tr><td/><td>Headline + Lead Para</td><td/><td/></tr><tr><td>1</td><td>TF-IDF</td><td>52.77</td><td>56.28</td></tr><tr><td>2</td><td>+ Ungrounded Mentions</td><td>53.34</td><td>56.86</td></tr><tr><td>3</td><td>+ Grounded Entities</td><td>55.03</td><td>58.57</td></tr><tr><td>4</td><td>+ Ungrounded+Grounded</td><td>55.18</td><td>58.86</td></tr><tr><td>5</td><td>TF-IDF + WMD</td><td>56.51</td><td>60.13</td></tr><tr><td>6</td><td>+ Ungrounded Mentions</td><td>57.04</td><td>60.82</td></tr><tr><td>7</td><td>+ Grounded Entities</td><td>57.4</td><td>61.47</td></tr><tr><td>8</td><td>+ Ungrounded+Grounded</td><td>58.05</td><td>61.78</td></tr></table>"
            },
            "TABREF5": {
                "type_str": "table",
                "html": null,
                "text": "Experimental results in percentage on S andS. S is a randomly constructed subset of the test set, andS is obtained by manually labeling samples in S.",
                "num": null,
                "content": "<table/>"
            },
            "TABREF6": {
                "type_str": "table",
                "html": null,
                "text": "A mis-prediction by TF-IDF corrected by the inclusion of WMD.",
                "num": null,
                "content": "<table><tr><td>Sides</td><td>-</td><td>Samples</td></tr><tr><td>citing</td><td>-</td><td>Chris Grayling was responding to a question from Gower Conservative MP Byron Davies about the regeneration investment fund for Wales \"and the underselling of a large amount of publicly owned property\".</td></tr><tr><td>cited</td><td>Ground Truth</td><td>Wales land deal leaves taxpayers 15m short A Welsh government spokesperson said there were conflicting valuations.</td></tr><tr><td/><td>Top-1 Prediction</td><td>Conservative MP compares Stephen Harper government to Jesus, inspiring hilarious #CPCJesus tweets Is it time we started referring to Prime Minister Stephen Harper as \"Our Lord and Saviour\"?</td></tr></table>"
            },
            "TABREF7": {
                "type_str": "table",
                "html": null,
                "text": "A correct prediction by TF-IDF but then changes into a mistake when including WMD.",
                "num": null,
                "content": "<table><tr><td>Sides</td><td>-</td><td>Samples</td><td/></tr><tr><td/><td/><td colspan=\"3\">Conservationists want the Bangladeshi government</td><td>to step up and help save the tigers</td></tr><tr><td/><td/><td/><td colspan=\"2\">Government of Bangladesh</td></tr><tr><td>citing</td><td>-</td><td colspan=\"3\">through greater administration and enforcement of anti-poaching laws, as Bangladeshi</td><td>does</td></tr><tr><td/><td/><td/><td/><td>Bangladesh</td></tr><tr><td/><td/><td colspan=\"3\">not legally protect tigers to the extent that other governments do, according to Inhabitat.</td></tr><tr><td/><td/><td>Bangladeshi's</td><td colspan=\"2\">abundant tiger population has collapsed to just 100</td></tr><tr><td/><td/><td>Bangladesh</td><td/></tr><tr><td/><td>Ground Truth</td><td>Bangladesh In Bangladeshi</td><td colspan=\"2\">Sundarbans , a new census shows that tiger populations in the Sundarbans</td><td>mangrove</td></tr><tr><td/><td/><td colspan=\"3\">forest are more endangered than ever. The study, which used hidden cameras to track and</td></tr><tr><td>cited</td><td/><td colspan=\"3\">record tigers, provides a more accurate update than previous surveys that used other methods.</td></tr><tr><td/><td/><td>Detroit Tigers</td><td>links: The Tigers are</td><td>in trouble</td></tr><tr><td/><td/><td>Detroit Tigers</td><td>Detroit Tigers</td></tr><tr><td/><td>Top-1 Prediction</td><td colspan=\"3\">After losing three straight games prior to All-Star break, the Major League Baseball All-Star Game</td><td>the Tigers don't Detroit Tigers</td></tr></table>"
            },
            "TABREF8": {
                "type_str": "table",
                "html": null,
                "text": "A mis-prediction by TF-IDF corrected by the inclusion of grounded entity features. The linked Wikipedia entries are indicated below the underlined entity mentions.",
                "num": null,
                "content": "<table><tr><td>Sides</td><td>-</td><td>Samples</td><td/></tr><tr><td/><td/><td colspan=\"3\">With activities at Westminster challenging</td><td>a narrow view of nationalism, and a planned</td></tr><tr><td/><td/><td colspan=\"3\">Parliament of the United Kingdom</td></tr><tr><td>citing</td><td>-</td><td colspan=\"3\">IrelandUnited Kingdom relations charm offensive across the UK and Ireland, it is</td><td>that the party intends to significantly expand</td></tr><tr><td/><td/><td colspan=\"3\">its reach beyond Scotland .</td></tr><tr><td/><td/><td/><td>Scotland</td></tr><tr><td/><td/><td>SNP launches</td><td colspan=\"2\">bid to extend influence beyond Scotland</td></tr><tr><td/><td/><td colspan=\"2\">Scottish National Party</td><td>Scotland</td></tr><tr><td/><td>Ground Truth</td><td colspan=\"3\">First Minister of Scotland and SNP leader Nicola Sturgeon</td><td>worked hard to reassure voters in</td></tr><tr><td>cited</td><td/><td colspan=\"2\">the election campaign.</td><td>Scottish National Party Nicola Sturgeon</td></tr><tr><td/><td/><td colspan=\"2\">Apple Pay UK launch</td><td>confirmed for mid-July</td></tr><tr><td/><td>Top-1 Prediction</td><td colspan=\"2\">Apple Pay United Kingdom</td></tr><tr><td/><td/><td colspan=\"3\">Leaked documents from retailers suggest a launch date early next week.</td></tr></table>"
            },
            "TABREF9": {
                "type_str": "table",
                "html": null,
                "text": "A mis-prediction by TF-IDF+ungrounded mention features corrected by the TF-IDF+grounded entity features. The linked Wikipedia entries are indicated below the underlined entity mentions.United Kindom The Daily Telegraph the government is now forcing farmers and food manufacturers to sell anywhere from 30-100% of their products to the state , as opposed to stores and supermarkets.",
                "num": null,
                "content": "<table><tr><td>Sides</td><td>-</td><td>Samples</td><td/><td/><td/></tr><tr><td/><td/><td colspan=\"3\">According to a UK Telegraph report,</td><td/></tr><tr><td>citing</td><td>-</td><td/><td/><td/><td/></tr><tr><td/><td/><td>Venezuelan</td><td colspan=\"3\">farmers ordered to hand over produce to state</td></tr><tr><td/><td/><td>Venezuela</td><td/><td/><td/></tr><tr><td/><td/><td colspan=\"2\">As Venezuela's</td><td colspan=\"3\">food shortages worsen, the president of the country's Food Industry</td></tr><tr><td/><td>Ground Truth</td><td>Venezuela</td><td/><td/><td/><td>Food Industry</td></tr><tr><td/><td/><td colspan=\"5\">Chamber has said that authorities ordered producers of milk , pasta, oil , rice</td><td>, sugar</td><td>and flour</td></tr><tr><td>cited</td><td/><td colspan=\"3\">to supply their products to the state stores.</td><td>milk</td><td>oil</td><td>rice</td><td>sugar</td></tr><tr><td/><td/><td colspan=\"2\">Welsh farmers</td><td colspan=\"2\">launch #NoLambWeek price campaign</td></tr><tr><td/><td/><td>United Kindom</td><td/><td/><td/></tr><tr><td/><td>Top-1 Prediction</td><td colspan=\"5\">Fed-up Welsh farmers United Kindom protest at the current slump in the UK lamb trade. are encouraging others to withhold their fat lambs for a week in</td></tr></table>"
            },
            "TABREF10": {
                "type_str": "table",
                "html": null,
                "text": "A correct prediction by TF-IDF but then changes into a mistake when including grounded entity features. The linked Wikipedia entries are indicated below the underlined entity mentions.",
                "num": null,
                "content": "<table/>"
            }
        }
    }
}