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% G.K.M. Tobin\titlenote{The secretary disavows

There are 3 interesting observations: 1) cleansing impacts Twitter entities and relevant documents.  This  is validated by the observation that recall  gains in Twitter entities and the relevant categories in the raw corpus also translate into overall performance gains. This observation implies that cleansing removes relevant and social documents than it does vital and news. That it removes relevant documents more than vital can be explained by the fact that cleansing removes the related links and adverts which may contain a mention of the entities. One example we saw was the the cleansing removed an image with a text of an entity name which was actually relevant. And that it removes social documents can be explained by the fact that most of the missing of the missing  docuemnts from cleansed are social. And all the docuemnts that are missing from raw corpus social. So in both cases socuial seem to suffer from text transformation and cleasing processes. 2) Taking both performance (recall at filtering and overall F-score during evaluation) into account, Wikipedia's canonical partial is the best entity profile for Wikipedia entities. This is interesting  to see that the retrieval of of  thousands vital-relevant document-entity pairs by name-variant partial does not translate to an increase in over all performance. It is even more interesting since canonical partial was not considered as contending profile for stream filtering by any of participant to the best of our knowledge. With this understanding, there  is actually no need to go and fetch different names variants from DBpedia, a saving of time and computational resources.

The deltas between entity profiles, relevance ratings, and document categories reveal four differences between Wikipedia and Twitter entities. 1) For Wikipedia entities, the difference between canonical partial and canonical is higher(16.1\%) than between name-variant partial and  name-variant(18.3\%).  This can be explained by saturation. This is to mean that documents have already been extracted by  name-variants and thus using their partials does not bring in many new relevant documents.  2) Twitter entities are mentioned by name-variant or name-variant partial and that is seen in the high recall achieved  compared to the low recall achieved by canonical(or their partial). This indicates that documents (specially news and others) almost never use user names to refer to Twitter entities. Name-variant partials are the best entity profiles for Twitter entities. 3) However, comparatively speaking, social documents refer to Twitter entities by their user names than news and others suggesting a difference in adherence to standard in names and naming. 4) Wikipedia entities achieve higher recall and higher overall performance. 

The high recall and subsequent higher overall performance of Wikipedia entities can  be due to two reasons. 1) Wikipedia entities are relatively well described than Twitter entities. The fact that we can retrieve different name variants from DBpedia is a measure of relatively rich description. Rich description plays a role in both filtering and computation of features such as similarity measures in later stages of the pipeline.   By contrast, we have only two names for Twitter entities: their user names and their display names which we collect from their Twitter pages. 2) There is not DBpedia-like resource for Twitter entities from which alternative names cane be collected.   

In the experimental results, we also observed that recall scores in the vital category are higher than in the relevant category. This observation  confirms one commonly held assumption:(frequency) mention is related to relevance.  this is the assumption why term frequency is used an indicator of document relevance in many information retrieval systems. The more  a document mentions an entity explicitly by name, the more likely the document is vital to the entity.

Across document categories, we observe a pattern in recall of others, followed by news, and then by social. Social documents are the hardest to retrieve. This can be explained by the fact that social documents (tweets and  blogs) are more likely to point to a resource where the entity is mentioned, mention the entities with some short abbreviation, or talk without mentioning the entities, but with some context in mind. By contrast news documents mention the entities they talk about using the common name variants more than social documents do. However, the greater difference in percentage recall between the different entity profiles in the news category indicates news refer to a given entity with different names, rather than by one standard name. By contrast others show least variation in referring to news. Social documents falls in between the two.  The deltas, for Wikipedia entities, between canonical partials and canonicals,  and name-variants and canonicals are high, an indication that canonical partials and name-variants bring in new relevant documents that can not be retrieved by canonicals. The rest of the two deltas are very small,  suggesting that partial names of name variants do not bring in new relevant documents. 

\section{Unfilterable documents}

There is a trade-off between using a richer entity-profile and retrieval of irrelevant documents. The richer the profile, the more relevant documents it retrieves, but also the more irrelevant documents. To put it into perspective, lets compare the number of documents that are retrieved with  canonical partial and with name-variant partial. Using the raw corpus, the former retrieves a total of 2547487 documents and achieves a recall of 72.2\%. By contrast, the later retrieves a total of 4735318 documents and achieves a recall of 90.2\%. The total number of documents extracted increases by 85.9\% for a recall gain of 18\%. The rest of the documents, that is 67.9\%, are newly introduced irrelevant documents. 

% 

 We observed that there are vital-relevant documents that we miss from raw only, and similarly from cleansed only. The reason for this is transformation from one format to another. The most interesting documents are those that we miss from both raw and cleansed corpus. We first identified the number of KB entities who have a vital relevance judgment and  whose documents can not be retrieved (they were 35 in total) and conducted a manual examination into their content to find out why they are missing. 

 We  observed  that among the missing documents, different document ids can have the same content, and be judged multiple times for a given entity.  %In the vital annotation, there are 88 news, and 409 weblog. 

 Avoiding duplicates, we randomly selected 35 documents, one for each entity.   The documents are 13 news and  22  social. Here below we have classified the situation under which a document can be vital for an entity without mentioning the entities with the different entity  profiles we used for filtering. 

 A good example is a weblog that talks about two north Dakota counties being drought disasters. The news is vital for Joshua Boschee, a politician, a member of North Dakota democratic party.  

\section{Conclusions}

In this paper, we examined the filtering stage of the entity-centric stream filtering and ranking  by holding the later stages of fixed. In particular, we studied the cleansing step, different entity profiles, type of entities(Wikipedia or Twitter), categories of documents(news, social, or others) and the relevance ratings. We attempted to address the following research questions: 1) does cleansing affect filtering and subsequent performance? 2) what is the most effective way of entity profiling? 3) is filtering different for Wikipedia and Twitter entities? 4) are some type of documents easily filterable and others not? 5) does a gain in recall at filtering step translate to a gain in F-measure at the end of the pipeline? and 6) what are the circumstances under which vital documents can not be retrieved? 

Cleansing does remove parts or entire contents of documents making them irretrievable. However, because of the introduction of false positives, recall gains by  raw corpus and some  richer entity profiles do not necessarily translate to overall performance gain. The results conclusion on this is mixed in the sense that cleansing helps improve the recall on vital documents and Wikipedia entities, but reduces the recall on Twitter entities and the relative category of relevance ranking. Vital and relevant documents show a difference in retrieval nonperformance documents are easier to filter than relevant.  

Despite an aggressive attempt to filter as many vital-relevant documents as possible,  we observe that there are still documents that we miss. While some are possible to retrieve with some modifications, some others are not. There are some document that indicate that an information filtering system does not seem to get them no matter how rich representation of entities they use. These circumstances under which this happens are many. We found that some documents have no content at all, subjectivity(it is not clear why some are judged vital). However, the main circumstances under which vital  documents can defy filtering is: outgoing link mentions, 

venue-event, entity - related entity, organization - main area of operation, entity - group, artist - artist's work,  party-politician, and world knowledge.  

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